CapStone Project_2025
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Diabetes Mellitus (DM) is a major public health challenge and predictors (risk factors) such as obesity, Age, and race and gender contribute to developing diabetes. Identifying predictors help in prevention and targeted intervention. Logistic regression is the standard statistical tool used to estimate the association between risk factors and binary outcomes, such as the presence or absence of diabetes. However, classical maximum likelihood estimation (MLE) can be unstable when there are small sample sizes, missing data, or situations of quasi- or complete separation in the data.
Healthcare data are complex, and the standard approaches to analyze them are not adequate (Zeger?) et al. (2020). They include DNA sequences, functional images of the brain, patient-reported outcomes, and electronic health records (EHR), survey records, sequences of health measurements, diagnoses, and treatments. The authors (Zeger?) et al. (2020) conducted Bayesian Hierarchical Model and MCMC, by combining prior knowledge and patient data (EHR) to predict the patient’s health status, trajectory, and/or likely benefits of intervention and using multivariate longitudinal patient data using R-packages to develop two-levels (1)time within person and (2) persons within a population and added co-variates (Exogenous: Age, clinical history and Endogenous:current treatment) on the individual’s multivariate health measurements. The model provided posterior distribution and an estimate of the marginal distribution of the regression coefficients by integration of Markov chain Monte Carlo (MCMC) and identified low-risk patient population with pneumonia etiology (children), prostate cancer, and mental disorders. Because it was entirely parametric (model limitations), an extensions to nonparametric or more flexible parametric models was recommended.
Bayesian Inference (parametric vs non-parametric) study conducted by Chatzimichail and Hatjimihail (2023) calculated the posterior probability of disease diagnosis by applying Bayesian inference in two modules comparing parametric (with a fixed set of parameters) and nonparametric distributions (which do not make a priori assumptions) on National Health and Nutrition Examination Survey data from two separate diagnostic tests on both diseased and non-diseased populations. They mentioned conventional methods based on clinical criteria and fixed numerical thresholds limit the information captured on the intricate relationship between diagnostic tests and the varying prevalence of diseases. The probability distributions associated with the outcomes overlap between the diseased and nondiseased groups.The dichotomous method fails to capture the complexity and heterogeneity of disease presentations across diverse populations. The applicability of the normal distribution (conventional method) is critiqued in dealing with skewness, bimodality, or multimodality and mentioned Bayesian nonparametric (vs parametric) diagnostic modeling as a Flexible distributional modeling for test outcomes (posterior disease probabilities). The Bayesian inference for posterior probability calculation using Wolfram Language and integration of prior probabilities distributions in both diseased and nondiseased populations enabled the evaluation of the combined data from multiple diagnostic tests with improved diagnostic accuracy, precision, and adaptability. The model showed flexibility, adaptability, and versatility in the diagnostic. They found Nonparametric Bayesian models a better fit for data distributions given the limited existing literature. Model was reported robust in capturing complex data patterns, producing multimodal probability patterns for disease, unlike the bimodal, double-sigmoidal curves seen with parametric models. The study limitations is the reliance on parametric models, limited scholarly publications and over-dependence on prior probabilities increase the uncertainties, resulting in broader confidence intervals for posterior probabilities. They mentioned systemic bias (unrepresentative datasets), incomplete datasets and absence of normative data compromises the accuracy of results, reliability and validity of Bayesian diagnostic methods and that combined with other statistical and computational techniques could enhance diagnostic capabilities, Chatzimichail and Hatjimihail (2023)
To understand the Bayesian methodology an overview (stages, development and advantages) by Schoot et al. (2021) specify the importance of the priors, data modeling, inferences, model checking and refinement, selecting a proper sampling technique from a posterior distribution, variational inferences, variable selection, and its application across various research fields. They applied Bayesian statistics across different fields (social sciences, ecology, genetics, medicine) in observed and unobserved parameters. They emphasize variable selection as a process of identifying the sub-set of predictors to include in a model especially where a large number of potential predictors are available. Unnecessary variables present issues such as multicollinearity, insufficient samples, overfitting the current data leading to poor predictive performance on new data making model interpretation difficult. Variables selection is best after checking correlations among the variables in the model (Eg: gene-to-gene interaction to predict genes in biomedical research).
Considering small sample size, Bayesian estimation with mildly informative priors is often. Based on the degree of (un)certainty (hyperparameters) surrounding the population parameter priors (informative, weakly informative and diffuse), prior distribution with a larger variance represents a greater amount of uncertainty. Prior elicitation could be through different ways (experts, generic expert, data-based, sample data using maximum likelihood or sample statistics). A prior sensitivity analysis of the likelihood that examines different forms of the model could assess how the priors and the likelihood align and impact on posterior estimates, reflecting variations not captured by the prior or the likelihood alone. Prior estimation allows data-informed shrinkage, regularization or influence algorithms towards a likely high-density region, and improves estimation efficiency. In a small sample i.e. less information, incorporation of priors strengthens the observed data and lends possible value(s) for the unknown parameter(s). To know probabilistic specification of the priors for a complex model with smaller sample sizes is important. In Bayesian inference, unknown parameters (random variables) have varied values, while the (observed) data have fixed values. The likelihood is a function of θ for the fixed data y. The likelihood function summarizes a statistical model that stochastically generates a range of possible values for θ and the observed data y. With priors and the likelihood of the observed data, the resulting posterior distribution provides an estimate of the unknown parameters, capture primary factors to improve our understanding. Monte Carlo technique provides integrals of sampled values from a given distribution through computer simulations. The packages BRMS and Blavaan in R are used for the probabilistic programming language Stan. MCMC algorithm only requires the probability distribution of interest to be specified up to a constant of proportionality and is scalable to high dimensions to obtain empirical estimates of the posterior distribution of interest. Bayesian inference adopts a simulation-based strategy for approximating posterior distributions.Frequentists do not consider the probability of the unknown parameters and consider to be fixed and likelihood is considered as the the conditional probability distribution p(y|θ) of the data (y), given fixed parameters (θ). Spatial and temporal variability are factored in Bayesian general linear models. A posterior distribution can simulate new data conditional on this distribution and assess, to providing valid predictions. The Bayesian approach in analyzing large-scale cancer genomic data, identifies novel molecular changes in cancer initiation and progression, the interactions between mutated genes, captured mutational signatures, key genetic interactions components, allow genomic-based patient stratification (clinical trials, personalized use of therapeutics) and in understanding cancer evolutionary processes. The mentioned the model reproducibility, reporting standards, and outlined a checklist. Limitations were related to the dependencies (autocorrelation of parameters over time in temporal model) and the subjectivity issue of priors.
The study by Klauenberg et al. (2015) emphasizes prior elicitation, analytical posteriors, robustness checks through guidance provided on Bayesian inference by performing Bayesian Normal linear regression, Core parametric (conjugate)model with Normal–Inverse-Gamma prior in metrology to calibrate instruments to evaluate inter-laboratory comparisons in determining fundamental constants.
In gaussian- Errors are independent and identically distributed, variance is unknown and is estimated from data, the relationship between X and Y is statistical, with noise and model uncertainty and the regression can not be treated as a measurement function . They mentioned statistical approaches (likelihood, Bayesian, bootstrap, etc.) could quantify uncertainty since Guide to the Expression of Uncertainty in Measurement (GUM) and its supplements are not applicable directly. They emphasized Bayesian inference that accounts for a priori information, and robustifies the analyses through steps including prior elicitation, posterior calculation, and robustness to prior uncertainty and model adequacy for the model development and about assumptions critical to Bayesian inference.
In Bayesian, unknowns such as (observables: data and unobservables: parameters and auxiliary variables) are random, are assigned probability distributions of the available information, and update prior knowledge about the unobservables with information about them contained in the data. The graphical representation of prior distribution and likelihood function, sensitivity analyses, or model checking enhances the elicitation and interpretation process.
For Normal linear regression (1) Normal inverse Gamma (NIG) distribution to a posterior is from the same family of (NIG) distribution. The NIG prior with known variance σ2 of observations is a conjugate prior distribution. Vague or non-informative prior distributions can be derived from NIG prior (2) alternative families (hierarchical priors) assign an additional layer of distributions to uncertain prior parameters or non-parametricriors.
Bayesian inference is influenced by - the uncertainty in the transformation of prior knowledge to prior distributions - the assumptions of the statistical model - the mistakes in data acquisition
Bayesian Hierarchical / meta-analytic linear regression model (DeLeeuw2012?) was developed to test of a formal method for augmenting data in linear regression analyses, by incorporating both exchangeable and unexchangeable information on regression coefficients (and standard errors) of previous studies.
The issue of multiple testing resulting in relatively low statistical power was highlighted which is problematic in null-hypothesis significance testing and highlighted that separate significance tests conducted for all regression coefficients, with the modest sample sizes, in different studies with different sets of statistically significant predictors, and addressing larger samples is practically unrealistic. In Linear regression analyses that do not account summary statistics from similar previous studies is affected in proving stability and precision of the parameter estimates. Lower estimate values are less certain and are affected by sampling variation.
Overcoming the issue of absence of formal studies, the study incorporated prior knowledge in Bayesian regression, to handle issues of increasing the sample size and they augmented the data of a new study incorporated priors on regression coefficients and standard errors from previous similar studies to solve the issue of the univariate case analysis and the issue of simultaneously combining multiple regression parameters per study, which ignore the relationship between the regression coefficients.
Bayesian linear regression by incorporating the evidence of specific predictors from different linear regression analyses (meta-analysis) provided a more acceptable solution to when previous study data are not (realistically) obtainable.
They emphasized - on information on predictors, based on the information on predictors from both previous and current data, the models are categorized into (1) Exchangable - when the current data and previous studies have the same set of predictors. (2) Unexchangable – when the predictors differ in the two studies.
A hierarchical model analyze parameters where studies are not-exchangeable. They found incorporating priors in a linear regression on new data yield a significantly better parameter estimate with an adequate approximation, encouraging performance gains and the large effects. Performance of the two versions (exchangeable and unexchangeable) of the replication model was consistently superior to using the current data alone.
The model offers better parameter estimates in a linear regression setting without expending time and energy to obtain data from the previous studies. Hierarchical unexchangeable model offers the advantage as it can address differences between studies and allows for explicit testing of the exchangeability assumption. Limitations is to have same set of predictors and the correlation issue between predictor variables. Leeuw and Klugkist (2012)
Bayesian logistic regression (Bayesian GLM) (Sequential clinical reasoning approach) model conducted on a longitudinal prospective cohort predicted the risk of incident cardiovascular disease by developing 3 models based on features added as predictor: (1) demographic features (basic model) (2) six metabolic syndrome components (metabolic score model) (3) conventional risk factors (enhanced model). The method used is Logistic Regression with incorporating priors on coefficients and sequential updating to predict individual-level CVD risk. The authors Liu et al. (2013) developed the model to overcome limited availability of molecular information in clinical practice (high cost and unavailability) that affected efficient disease diagnosis. They mention Sequential clinical reasoning approach) model is an alternative approach to analyze data to efficiently identify a high-risk population based on the routinely checked biological markers before doing these expensive molecular tests.
Model such as Framingham Risk Score method are insufficient because of Heterogeneity (geographic, ethnic group, variations, and social contextual network) observed in the data which is unobservable and unmeasurable and required construction of separate models.
Sequential clinical reasoning approach analyzed subjects enrolled in a screening program (20–79 years) in the Keelung city of Taiwan, followed for 5 years and classified incident cancers and chronic diseases (cardiovascular disease) by incorporating (1) standardized risk score (MetS: fasting glucose, blood pressure, HDL-C, triglyceride and waist circumference) (2) risk factors: gender, heredity, smoking, etc.
The methodology develop three models based on sequential manner that emulated a clinician’s evaluation process. The model considered the normal distribution of regression coefficients of all predictors, allowing for uncertainty of clinical weights and the credible intervals of predicted risk estimates were averaged. The individual risk is elicited by prior speculation (first impression) that is updated by objective observed data (patient’s history and laboratory findings). Regression coefficients for computing risk score were treated as random variable with normal distribution rather than a fixed value (traditional risk prediction model by frequentist). The updated prior distribution with the likelihood of the current data provided a posterior distribution to predict the risk for a specific disease.
The enhanced model showed better performance where conventional risk factors were incorporated. Patients’ background contributed significantly to baseline risk. Even with ecological heterogeneity, the regression model adopted individual characteristics and made individual risk prediction for the CVD incidence. The limitation of the model mentions the interactions between predictors and cross-validated through external validation by applying the proposed models to new subjects not included in the training of the model parameters.
Bayesian Multiple Imputation and Logistic regression was conducted by Austin et al. (2021) on missing data, from clinical research. They mentioned missing values are not measured or recorded for all subjects are due to varied reasons: (i) patient refusal to respond to specific questions (ii) loss of patient to follow-up; (iii)investigator or mechanical error (iv) physicians not ordering certain investigationsfor some patients. The study emphasizes on understanding the type of missing data (MAR, MNAR, MCAR). Multiple imputation (MI) address missing data, provides multiple plausible values for missing values of a given variable resulting in creation of multiple completed data sets, conducts identical statistical analyses on each completed data sets, and the pooled results from across complete data sets, are then analyzed. The study conducted MI, mentioning steps in implementation and development, emphasized on the number of imputed data sets and addressed on creating and derived variables. MI was applied missing data using (R, SAS, and Stata) to analyze patients with heart failure to estimate the probability of 1-year mortality. Austin et al. (2021).
Our study aims to apply Bayesian logistic regression on a dataset with quasi-separation and missing values with the resultant small sample size. It is a retrospective study to analyze NHANES survey data collected between 2013-2014 aimed to predict diabetes status based on predictors (body mass index (BMI), Age, gender, and race). Our data exploration initially showed 9813 observations and selected 5 variables, later on complete case analysis and listwise deletion of missing data, the effective sample size was reduced. Since, small sample size and quasi-separation with implausibly large coefficients and unstable estimates is as a challenge for traditional logistic regression models, motivated us to apply multiple imputation (MICE) and Bayesian logistic regression to provide a flexible framework for modeling uncertainty and incorporating prior knowledge and avoiding the issue of separation and small dataset.
Statistical Tool used is R, R packages, and libraries to import, manage and analyze the data. Data collected is from NHANES 2-year cross-sectional data (2013-2014 year) using 3 datasets (demographics, exam, questionnaire) Center for Health Statistics (1999). Using haven package .XPT files were imported in r-studio modified to dataframe (df).
# loading packages
options(repos = c(CRAN = "https://cloud.r-project.org"))
install.packages("nhanesA")
library ("nhanesA")
library(tidyverse)
library(knitr)
library(ggthemes)
library(ggrepel)
library(dslabs)
library(Hmisc)
library(dplyr)
library(tidyr)
library(forcats)
library(ggplot2)
library(classpackage)
library(janitor)
install.packages("gt")
library(gt)
library(survey)
library(DataExplorer)
library(logistf)Data Management
Subsets created from the original weighted 3 datasets (demographics, exam, questionnaire) were merged into a single dataframe for analysis and exploration. The merged dataframe included selected variables of interest, was cleaned, variables categorized, and recoded and analyzed. Basic statistics, anamolies and patterns reported before running Bayesian regression. Final dataset included weighted means of all selected variables of interest. Below tabel describes the details of all variable in the data.
Response Variable (Binary, Diabetes) was defined as - “Is there one Dr you see for diabetes”
Predictor Variables (Body Mass Index, factor, 4 levels)
The original data has BMDBMIC (measured BMI) as categorical and had no missing values. It (BMI) has the following 4 levels:
o Underweight (<5th percentile)
o Normal (5th–<85th)
o Overweight (85th–<95th) o Obese (≥95th percentile)
o Missing We kept it as it is as categorization provides clinically interpretable groups
Covariates:
library(gt)
# formation of table with variable details
variables <- c("ID", "BMI" , "Age", "Gender" , "Race", "PSU", "Strata", "Weight" , "Diabetes")
df <- data.frame(Variable = variables, Description = descriptions <- c("Respondent sequence number",
"BMI calculated as weight in kilograms divided by height in meters squared, and then rounded to one decimal place.",
"Age in years of the participant at the time of screening. Individuals 80 and over are topcoded at 80 years of Age.",
"Gender",
"Race/ethnicity Recode of reported race and Hispanic origin information",
"Sample PSU",
"Sample strata",
"MEC exam weight",
"Diabetes status Is there one doctor or other health professional {you usually see/SP usually sees} for {your/his/her} diabetes? Do not include specialists to whom {you have/SP has} been referred such as diabetes educators, dieticians or foot and eye doctors."))
df %>%
gt %>%
tab_header(
title = "Table Variable Description"
) %>%
tab_footnote(
footnote = "Each variable in the dataset, accompanied by a qualitative description from the study team."
)| Table Variable Description | |
|---|---|
| Variable | Description |
| ID | Respondent sequence number |
| BMI | BMI calculated as weight in kilograms divided by height in meters squared, and then rounded to one decimal place. |
| Age | Age in years of the participant at the time of screening. Individuals 80 and over are topcoded at 80 years of Age. |
| Gender | Gender |
| Race | Race/ethnicity Recode of reported race and Hispanic origin information |
| PSU | Sample PSU |
| Strata | Sample strata |
| Weight | MEC exam weight |
| Diabetes | Diabetes status Is there one doctor or other health professional {you usually see/SP usually sees} for {your/his/her} diabetes? Do not include specialists to whom {you have/SP has} been referred such as diabetes educators, dieticians or foot and eye doctors. |
| Each variable in the dataset, accompanied by a qualitative description from the study team. | |
The data structure, a plot of the data and the breakdownof the missingness is presented
# weighted means of each variable
str(merged_data)'data.frame': 9813 obs. of 9 variables:
$ ID : num 73557 73558 73559 73560 73561 ...
$ BMI : Factor w/ 4 levels "Underweight",..: NA NA NA 2 NA NA NA NA NA NA ...
$ Age : num 69 54 72 9 73 56 0 61 56 65 ...
$ Gender : Factor w/ 2 levels "Male","Female": 1 1 1 1 2 1 1 2 2 1 ...
$ Race : Factor w/ 5 levels "Mexican American",..: 4 3 3 3 3 1 3 3 3 3 ...
$ PSU : num 1 1 1 2 2 1 1 1 1 2 ...
$ Strata : num 112 108 109 109 116 111 105 114 112 112 ...
$ Weight : num 13481 24472 57193 55767 65542 ...
$ Diabetes: Factor w/ 2 levels "Yes","No": 1 1 2 NA NA NA NA NA NA NA ...plot_str(merged_data)
introduce(merged_data) rows columns discrete_columns continuous_columns all_missing_columns
1 9813 9 4 5 0
total_missing_values complete_rows total_observations memory_usage
1 15381 14 88317 554872plot_intro(merged_data, title="Figure 1 (Merged dataset). Structure of variables and missing observations.")
plot_missing(merged_data, title="Figure 2(Merged dataset). Breakdown of missing observations.")
Most cases are non-hispanic whites.
Of those who reported their diabetes status, shows more counts reported having diabetes.
Genders are relatively evenly distributed.
Majority population were in the normal range of BMI
Histograms (Figure 5) shows frequency distributions for Age, with slight right skewness.
# Correct survey design
nhanes_design <- svydesign(
id = ~PSU, # primary sampling unit
strata = ~Strata, # stratification variable
weights = ~Weight,# survey weights
data = merged_data,
nest = TRUE
)
# Weighted proportion of Diabetes
svymean(~Diabetes, design = nhanes_design, na.rm = TRUE) mean SE
DiabetesYes 0.78759 0.021
DiabetesNo 0.21241 0.021svymean(~Age , design = nhanes_design, na.rm = TRUE) mean SE
Age 37.504 0.4412svymean(~BMI, design = nhanes_design, na.rm = TRUE) mean SE
BMIUnderweight 0.037703 0.0037
BMINormal weight 0.628530 0.0120
BMIOverweight 0.162217 0.0056
BMIObese 0.171551 0.0109svymean(~Gender, design = nhanes_design, na.rm = TRUE) mean SE
GenderMale 0.48861 0.0065
GenderFemale 0.51139 0.0065svymean(~Race, design = nhanes_design, na.rm = TRUE) mean SE
RaceMexican American 0.110450 0.0199
RaceOther Hispanic 0.060637 0.0106
RaceNon-Hispanic White 0.622315 0.0365
RaceNon-Hispanic Black 0.120895 0.0173
RaceOther Race - Including Multi-Racial 0.085704 0.0076Using library(survey), we extracts the weighted means and sd of variables from the data having 9813 observations.
library(dplyr)
library(knitr)
# 1. continuous variable summary
cont_summary <- merged_data %>%
summarise(
Mean = round(mean(Age, na.rm = TRUE), 2),
SD = round(sd(Age, na.rm = TRUE), 2),
Min = min(Age, na.rm = TRUE),
Max = max(Age, na.rm = TRUE)
) %>%
mutate(
Variable = "Age",
Category = "Continuous",
Count = nrow(merged_data) - sum(is.na(merged_data$Age)),
Proportion = NA
) %>%
select(Variable, Category, Count, Proportion, Mean, SD, Min, Max)
# 2. categorical variables
cat_summary <- function(df, var, name) {
df %>%
count({{var}}) %>%
mutate(
Proportion = round(n / sum(n), 3),
Variable = name,
Mean = NA, SD = NA, Min = NA, Max = NA
) %>%
rename(Category = {{var}}, Count = n) %>%
select(Variable, Category, Count, Proportion, Mean, SD, Min, Max)
}
# 3. summaries for categorical variables
gender_summary <- cat_summary(merged_data, Gender, "Gender")
BMI_summary <- cat_summary(merged_data, BMI, "BMI Category")
race_summary <- cat_summary(merged_data, Race, "Race")
diab_summary <- cat_summary(merged_data, Diabetes, "Diabetes")
# 4. combine all into one table
final_table <- bind_rows(
cont_summary,
gender_summary,
BMI_summary,
race_summary,
diab_summary
)
# 5. display as a table
kable(final_table, caption = "Table 1. Descriptive Statistics of Study Variables")| Variable | Category | Count | Proportion | Mean | SD | Min | Max |
|---|---|---|---|---|---|---|---|
| Age | Continuous | 9813 | NA | 31.63 | 24.4 | 0 | 80 |
| Gender | Male | 4831 | 0.492 | NA | NA | NA | NA |
| Gender | Female | 4982 | 0.508 | NA | NA | NA | NA |
| BMI Category | Underweight | 132 | 0.013 | NA | NA | NA | NA |
| BMI Category | Normal weight | 2167 | 0.221 | NA | NA | NA | NA |
| BMI Category | Overweight | 595 | 0.061 | NA | NA | NA | NA |
| BMI Category | Obese | 629 | 0.064 | NA | NA | NA | NA |
| BMI Category | NA | 6290 | 0.641 | NA | NA | NA | NA |
| Race | Mexican American | 1685 | 0.172 | NA | NA | NA | NA |
| Race | Other Hispanic | 930 | 0.095 | NA | NA | NA | NA |
| Race | Non-Hispanic White | 3538 | 0.361 | NA | NA | NA | NA |
| Race | Non-Hispanic Black | 2198 | 0.224 | NA | NA | NA | NA |
| Race | Other Race - Including Multi-Racial | 1462 | 0.149 | NA | NA | NA | NA |
| Diabetes | Yes | 553 | 0.056 | NA | NA | NA | NA |
| Diabetes | No | 169 | 0.017 | NA | NA | NA | NA |
| Diabetes | NA | 9091 | 0.926 | NA | NA | NA | NA |
library(gt)
# Cross-tabulation: Diabetes vs BMI
tab1 <- table(merged_data$Diabetes, merged_data$BMI, useNA = "ifany")
prop.table(tab1) * 100 # overall percentages
Underweight Normal weight Overweight Obese <NA>
Yes 0.00000000 0.05095282 0.03057169 0.02038113 5.53347600
No 0.00000000 0.02038113 0.02038113 0.00000000 1.68144298
<NA> 1.34515439 22.01161724 6.01243249 6.38948334 56.88372567# Cross-tabulation: Race vs Diabetes
tab2 <- table(merged_data$Race, merged_data$Diabetes, useNA = "ifany")
prop.table(tab2) * 100 # overall percentages
Yes No <NA>
Mexican American 0.8763885 0.4178131 15.8768980
Other Hispanic 0.4891470 0.1528585 8.8352186
Non-Hispanic White 2.0992561 0.5706716 33.3842862
Non-Hispanic Black 1.4878223 0.3668603 20.5441761
Other Race - Including Multi-Racial 0.6827678 0.2140018 14.0018343# Cross-tabulation: Gender vs Diabetes
tab3 <- table(merged_data$Gender, merged_data$Diabetes, useNA = "ifany")
prop.table(tab3) * 100 # overall percentages
Yes No <NA>
Male 2.6903088 0.8865790 45.6537247
Female 2.9450729 0.8356262 46.9886885## Bar plot of Age, gender, race, diabetes status, BMI ##
plot_bar(merged_data, title = "Figure 3(Merged dataset). Frequency plots of categorical variables.")
Method
We performed Bayesian Logistic Regression Model statistical analysis on our data based on the data characteristics as - binary outcome (Diabetes (yes/ no) -binomial based on probability Bayes rules - bayes rule linear relation between (predictor) X and (response) Y - regression discrete variable that can have two values, 0 or 1 (Bernoulli probability model) Classification tasks in regression analyze of categorical response variables -predicting or classifying the response category.
We later compare the two models
Below are the principles, concept and assumptions of the two models
The anomalies in data (quasi-separation was handled by performing Firth regression and the small dataset due to listwise deletion was managed by performing Multivariate Imputation by Chained Equations (MICE).
Concept and results of Firth regression and MICE presented below.
Firth (penalized) regression was considered to handle quasi-separation, D’Angelo and Ran (2025). Firth regression, a frquentist approach that use Jeffreys prior for bias correction. It does not provide posterior and no sampling using MCMC (vs) bayesian logisitic regression.
Below are the summaries from the MLR (raw data), Baseline model regression, Firth regression
Interpretation of the 14 non-missing cases - could not be trusted because of small sample size and the separation problem - Models with all predictors together and with sequential adding of predictors, all models showed unstable and extreme estimates with standard errors not meaningful. - Adding more predictors makes the deviance drop but indicated overfitting / separation, not true explanatory power. - BMI alone contributes very little - Race and gender make models appear stronger, but was based on small sample (n=14) and shows a case of complete separation, not generalizable evidence.
We decided to perform imputation, to retain full N = ~9813 to deal with small sample size and avoid quasi-separation.
## Multiple Imputation performed
# Subset variables for imputation in analytic_data df
library(dplyr)
library(ggplot2)
library(mice)
library(VIM)
library(janitor)
# 1. Select variables for imputation
vars <- c("ID", "BMI", "Age", "Gender", "Race", "PSU", "Strata", "Weight", "Diabetes")
analytic_data <- merged_data[, vars]
glimpse(analytic_data)Rows: 9,813
Columns: 9
$ ID <dbl> 73557, 73558, 73559, 73560, 73561, 73562, 73563, 73564, 73566…
$ BMI <fct> NA, NA, NA, Normal weight, NA, NA, NA, NA, NA, NA, NA, Normal…
$ Age <dbl> 69, 54, 72, 9, 73, 56, 0, 61, 56, 65, 26, 9, 76, 10, 10, 33, …
$ Gender <fct> Male, Male, Male, Male, Female, Male, Male, Female, Female, M…
$ Race <fct> Non-Hispanic Black, Non-Hispanic White, Non-Hispanic White, N…
$ PSU <dbl> 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 1…
$ Strata <dbl> 112, 108, 109, 109, 116, 111, 105, 114, 112, 112, 113, 115, 1…
$ Weight <dbl> 13481.042, 24471.770, 57193.285, 55766.512, 65541.871, 25344.…
$ Diabetes <fct> Yes, Yes, No, NA, NA, NA, NA, NA, NA, NA, NA, NA, No, NA, NA,…glimpse(merged_data)Rows: 9,813
Columns: 9
$ ID <dbl> 73557, 73558, 73559, 73560, 73561, 73562, 73563, 73564, 73566…
$ BMI <fct> NA, NA, NA, Normal weight, NA, NA, NA, NA, NA, NA, NA, Normal…
$ Age <dbl> 69, 54, 72, 9, 73, 56, 0, 61, 56, 65, 26, 9, 76, 10, 10, 33, …
$ Gender <fct> Male, Male, Male, Male, Female, Male, Male, Female, Female, M…
$ Race <fct> Non-Hispanic Black, Non-Hispanic White, Non-Hispanic White, N…
$ PSU <dbl> 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 1…
$ Strata <dbl> 112, 108, 109, 109, 116, 111, 105, 114, 112, 112, 113, 115, 1…
$ Weight <dbl> 13481.042, 24471.770, 57193.285, 55766.512, 65541.871, 25344.…
$ Diabetes <fct> Yes, Yes, No, NA, NA, NA, NA, NA, NA, NA, NA, NA, No, NA, NA,…# 2. Run mice to create 5 imputed datasets
imputed_data <- mice(
analytic_data,
m = 5, # number of imputed datasets
method = 'pmm', # predictive mean matching
seed = 123
)
iter imp variable
1 1 BMI Diabetes
1 2 BMI Diabetes
1 3 BMI Diabetes
1 4 BMI Diabetes
1 5 BMI Diabetes
2 1 BMI Diabetes
2 2 BMI Diabetes
2 3 BMI Diabetes
2 4 BMI Diabetes
2 5 BMI Diabetes
3 1 BMI Diabetes
3 2 BMI Diabetes
3 3 BMI Diabetes
3 4 BMI Diabetes
3 5 BMI Diabetes
4 1 BMI Diabetes
4 2 BMI Diabetes
4 3 BMI Diabetes
4 4 BMI Diabetes
4 5 BMI Diabetes
5 1 BMI Diabetes
5 2 BMI Diabetes
5 3 BMI Diabetes
5 4 BMI Diabetes
5 5 BMI Diabetes# 3. First imputed dataset
Imputed_data1 <- complete(imputed_data, 1)
# 4. Check missingness
str(Imputed_data1)'data.frame': 9813 obs. of 9 variables:
$ ID : num 73557 73558 73559 73560 73561 ...
$ BMI : Factor w/ 4 levels "Underweight",..: 4 2 3 2 3 2 2 3 4 2 ...
$ Age : num 69 54 72 9 73 56 0 61 56 65 ...
$ Gender : Factor w/ 2 levels "Male","Female": 1 1 1 1 2 1 1 2 2 1 ...
$ Race : Factor w/ 5 levels "Mexican American",..: 4 3 3 3 3 1 3 3 3 3 ...
$ PSU : num 1 1 1 2 2 1 1 1 1 2 ...
$ Strata : num 112 108 109 109 116 111 105 114 112 112 ...
$ Weight : num 13481 24472 57193 55767 65542 ...
$ Diabetes: Factor w/ 2 levels "Yes","No": 1 1 2 1 1 2 1 1 1 2 ...summary(Imputed_data1) ID BMI Age Gender
Min. :73557 Underweight : 218 Min. : 0.00 Male :4831
1st Qu.:76092 Normal weight:5107 1st Qu.:10.00 Female:4982
Median :78643 Overweight :1831 Median :27.00
Mean :78645 Obese :2657 Mean :31.63
3rd Qu.:81191 3rd Qu.:52.00
Max. :83731 Max. :80.00
Race PSU Strata
Mexican American :1685 Min. :1.000 Min. :104.0
Other Hispanic : 930 1st Qu.:1.000 1st Qu.:107.0
Non-Hispanic White :3538 Median :1.000 Median :111.0
Non-Hispanic Black :2198 Mean :1.483 Mean :110.9
Other Race - Including Multi-Racial:1462 3rd Qu.:2.000 3rd Qu.:115.0
Max. :2.000 Max. :118.0
Weight Diabetes
Min. : 3748 Yes:7252
1st Qu.: 13187 No :2561
Median : 20965
Mean : 31713
3rd Qu.: 37922
Max. :171395 colSums(is.na(Imputed_data1)) ID BMI Age Gender Race PSU Strata Weight
0 0 0 0 0 0 0 0
Diabetes
0 plot_intro(Imputed_data1, title="Figure 8. Structure of variables and missing observations.")
plot_bar(Imputed_data1, title = "Figure 9. Frequency plots of categorical variables.")
plot_correlation(na.omit(Imputed_data1[, c("BMI", "Diabetes")]), maxcat=5L, title = "Figure")
# 5. Cross-tabulation
tab <- table(Imputed_data1$BMI, Imputed_data1$Diabetes)
print(tab)
Yes No
Underweight 159 59
Normal weight 3760 1347
Overweight 1342 489
Obese 1991 666# Chi-square test
chisq.test(tab)
Pearson's Chi-squared test
data: tab
X-squared = 2.1289, df = 3, p-value = 0.5461# Cross-tabulation
# BMI vs Diabetes
tab_BMI <- table(Imputed_data1$BMI, Imputed_data1$Diabetes)
print(tab_BMI)
Yes No
Underweight 159 59
Normal weight 3760 1347
Overweight 1342 489
Obese 1991 666prop.table(tab_BMI, 1) * 100 # row percentages
Yes No
Underweight 72.93578 27.06422
Normal weight 73.62444 26.37556
Overweight 73.29328 26.70672
Obese 74.93414 25.06586# Gender vs Diabetes
tab_gender <- table(Imputed_data1$Gender, Imputed_data1$Diabetes)
prop.table(tab_gender, 1) * 100
Yes No
Male 72.92486 27.07514
Female 74.84946 25.15054# Race vs Diabetes
tab_race <- table(Imputed_data1$Race, Imputed_data1$Diabetes)
prop.table(tab_race, 1) * 100
Yes No
Mexican American 71.03858 28.96142
Other Hispanic 74.40860 25.59140
Non-Hispanic White 76.17298 23.82702
Non-Hispanic Black 72.47498 27.52502
Other Race - Including Multi-Racial 73.52941 26.47059# Age vs Diabetes
tab_age <- table(Imputed_data1$Age, Imputed_data1$Diabetes)
head (prop.table(tab_age, 1) * 100)
Yes No
0 74.93606 25.06394
1 74.89879 25.10121
2 72.51908 27.48092
3 74.64115 25.35885
4 73.48837 26.51163
5 67.17172 32.82828# Breakdown of Diabetes within BMI
breakdown_BMI <- Imputed_data1 %>%
group_by(BMI, Diabetes) %>%
summarise(Count = n(), .groups = "drop") %>%
group_by(BMI) %>%
mutate(
Percent = round(100 * Count / sum(Count), 1)
)
breakdown_BMI# A tibble: 8 × 4
# Groups: BMI [4]
BMI Diabetes Count Percent
<fct> <fct> <int> <dbl>
1 Underweight Yes 159 72.9
2 Underweight No 59 27.1
3 Normal weight Yes 3760 73.6
4 Normal weight No 1347 26.4
5 Overweight Yes 1342 73.3
6 Overweight No 489 26.7
7 Obese Yes 1991 74.9
8 Obese No 666 25.1# 6. Frequency tables for categorical variables
categorical_vars <- c("BMI", "Gender", "Race", "Diabetes")
for (var in categorical_vars) {
cat("\nFrequency table for", var, ":\n")
print(table(Imputed_data1[[var]]))
print(round(prop.table(table(Imputed_data1[[var]])), 3))
}
Frequency table for BMI :
Underweight Normal weight Overweight Obese
218 5107 1831 2657
Underweight Normal weight Overweight Obese
0.022 0.520 0.187 0.271
Frequency table for Gender :
Male Female
4831 4982
Male Female
0.492 0.508
Frequency table for Race :
Mexican American Other Hispanic
1685 930
Non-Hispanic White Non-Hispanic Black
3538 2198
Other Race - Including Multi-Racial
1462
Mexican American Other Hispanic
0.172 0.095
Non-Hispanic White Non-Hispanic Black
0.361 0.224
Other Race - Including Multi-Racial
0.149
Frequency table for Diabetes :
Yes No
7252 2561
Yes No
0.739 0.261 # 7. Summary statistics for continuous variables
continuous_vars <- c("Age")
for (var in continuous_vars) {
cat("\nSummary statistics for", var, ":\n")
print(summary(Imputed_data1[[var]]))
print(paste("SD:", round(sd(Imputed_data1[[var]]), 2)))
}
Summary statistics for Age :
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.00 10.00 27.00 31.63 52.00 80.00
[1] "SD: 24.4"# 8. Bar plots for categorical variables
for (var in categorical_vars) {
ggplot(Imputed_data1, aes_string(x = var)) +
geom_bar(fill = "steelblue") +
labs(title = paste("Bar plot of", var), y = "Count") +
theme_minimal() -> p
print(p)
}
# 9. Histograms for continuous variables
for (var in continuous_vars) {
ggplot(Imputed_data1, aes_string(x = var)) +
geom_histogram(fill = "skyblue", color = "black", bins = 30) +
labs(title = paste("Histogram of", var), y = "Frequency") +
theme_minimal() -> p
print(p)
}
# 10. Scatter plot example (BMI vs Age)
ggplot(Imputed_data1, aes(x = Age, y = BMI, color = BMI)) +
geom_point(alpha = 0.6) +
labs(title = "Scatter plot of BMI vs Age", y = "BMI", x = "Age") +
theme_minimal()
# 11. Relative breakdown of Diabetes by BMI
ggplot(breakdown_BMI, aes(x = BMI, y = Percent, fill = Diabetes)) +
geom_col(position = "fill") +
scale_y_continuous(labels = scales::percent) +
labs(
title = "Relative Breakdown of Diabetes by BMI Category",
x = "BMI Category", y = "Proportion"
) +
theme_minimal()
# 12. Crosstab with percentages
Imputed_data1 %>%
tabyl(Diabetes, BMI) %>%
adorn_percentages("col") Diabetes Underweight Normal weight Overweight Obese
Yes 0.7293578 0.7362444 0.7329328 0.7493414
No 0.2706422 0.2637556 0.2670672 0.2506586# 13. Margin plot for BMI vs Diabetes
marginplot(
Imputed_data1[, c("BMI", "Diabetes")],
col = mdc(1:2, trans = FALSE),
cex = 1.2,
cex.lab = 1.2,
cex.numbers = 1.3,
pch = 19
)
Results from MICE:
MI resulted in filling imputed values with resulting 9813 observations with no NAs. A comparative bar plot on missingness in the raw data and the imputed data is presented below.
A heatmap of the imputed dataset generated a correlation between BMI categories and Diabetes status. BMI dummy variables are strongly negatively correlated.
There was no strong linear association between BMI category and diabetes in the dataset. Chi-square calculation of categorical varaibels revealed p-value = 0.5461, which is > 0.05 with no evidence of association. Imputed data check in marginal plot- The margin plot shows that the distribution of imputed points is consistent with observed data (no strange outliers)
The Logistic regression model is:
\[ \text{logit}(P(Y_i=1)) = \beta_0 + \beta_1 \cdot Age_i + \beta_2 \cdot BMI_i + \beta_3 \cdot HTN_i + \beta_4 \cdot HDL_i + \beta_5 \cdot (HTN_i \times HDL_i) \]
Linear Regression equation:
\[ y_i = \beta_0 + \beta_1 X_1 +\varepsilon_i \]
Comparison of multiple imputation and Bayesian data augmentation
| Multiple imputation | Bayesian data augmentation |
|---|---|
|
|
|
|
|
|
Diagnostics performed before regression analysis
# MLR on imputed data
# assumption check (# for correlation # )
# correlation matrix
# Frequentist logistic regression on imputed data
m_imp <- glm(Diabetes ~ Age + Gender + Race + BMI,
data = Imputed_data1,
family = binomial)
summary(m_imp)
Call:
glm(formula = Diabetes ~ Age + Gender + Race + BMI, family = binomial,
data = Imputed_data1)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.761926 0.162425 -4.691 2.72e-06
Age -0.004728 0.001011 -4.678 2.89e-06
GenderFemale -0.092730 0.046133 -2.010 0.04442
RaceOther Hispanic -0.154250 0.092549 -1.667 0.09558
RaceNon-Hispanic White -0.212663 0.067746 -3.139 0.00169
RaceNon-Hispanic Black -0.055537 0.072128 -0.770 0.44131
RaceOther Race - Including Multi-Racial -0.109388 0.080345 -1.361 0.17336
BMINormal weight 0.024339 0.156361 0.156 0.87630
BMIOverweight 0.071544 0.162658 0.440 0.66005
BMIObese 0.020287 0.161137 0.126 0.89981
(Intercept) ***
Age ***
GenderFemale *
RaceOther Hispanic .
RaceNon-Hispanic White **
RaceNon-Hispanic Black
RaceOther Race - Including Multi-Racial
BMINormal weight
BMIOverweight
BMIObese
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 11267 on 9812 degrees of freedom
Residual deviance: 11219 on 9803 degrees of freedom
AIC: 11239
Number of Fisher Scoring iterations: 4coef(m_imp) (Intercept) Age
-0.761925862 -0.004727998
GenderFemale RaceOther Hispanic
-0.092730385 -0.154250348
RaceNon-Hispanic White RaceNon-Hispanic Black
-0.212662854 -0.055536906
RaceOther Race - Including Multi-Racial BMINormal weight
-0.109388375 0.024339471
BMIOverweight BMIObese
0.071543638 0.020286793 confint(m_imp) 2.5 % 97.5 %
(Intercept) -1.086867141 -0.449109500
Age -0.006713516 -0.002751496
GenderFemale -0.183172208 -0.002320639
RaceOther Hispanic -0.336602276 0.026303770
RaceNon-Hispanic White -0.345137536 -0.079535197
RaceNon-Hispanic Black -0.196779898 0.086003952
RaceOther Race - Including Multi-Racial -0.267119094 0.047893182
BMINormal weight -0.276189156 0.337871931
BMIOverweight -0.241855094 0.396816724
BMIObese -0.289985572 0.342735874# Log-odds (link)
Imputed_data1$logit <- predict(m_imp, type = "link") ## log (Odds)
# Probability
Imputed_data1$prob <- exp(Imputed_data1$logit) / (1 + exp(Imputed_data1$logit)) # prob
# Plot predicted probability vs Age
ggplot(Imputed_data1, aes(x = Age, y = prob)) +
geom_point(alpha = 0.3) +
geom_smooth(method = "loess") +
labs(x = "Age", y = "Predicted Probability of Diabetes")
Findings from regression of MI data set
Relationship between Age and log-odds of diabetes are roughly linear but not perfectly, but are acceptable for logistic regression assumptions.
Generalized Variance Inflation Factor (vif- adjusted report there is no collinearity between predictors (GVIF between ~1.0–1.04) and we can run model without removing or dropping or combining variables.
Cooks distance and influential points, we found - Most data points are safe, not influencing the model In the data with (~9813 cases), cutoff ≈ 0.0004. A cluster at high leverage shows unusual predictor values, but not high influence. A few above Cook’s Distance cutoff: worth checking individually, but no major threat to model stability. no outliers detected (not suspected = 9813)
Results from Hosmer–Lemeshow (H–L) at alpha =0.05, with p < 0.001, we find our logistic regression model does not fit the data well.
Graph shows Residual vs fitted (imputed data model)
For males, the informative prior Ali et al. (2024), we use is
Normal(μ = 1.705, σ² = 0.448²).
# Fitted values and residuals
fitted_imputed1 <- fitted(m_imp)
residual_imputed1 <- residuals(m_imp)
# Residuals vs Fitted plot
plot(fitted_imputed1, residual_imputed1,
xlab = "Fitted probabilities",
ylab = "Residuals",
main = "Residuals vs Fitted")
abline(h = 0, col = "red", lty = 2)
# Collinearity check
library(car)
vif(m_imp) # VIF > 5 indicates multicollinearity GVIF Df GVIF^(1/(2*Df))
Age 1.108163 1 1.052693
Gender 1.002032 1 1.001015
Race 1.040913 4 1.005025
BMI 1.078206 3 1.012629# Influential points
library(broom)
influence_m_imp <- broom::augment(m_imp)
# Plot Cook's distance
ggplot(influence_m_imp, aes(x = seq_along(.cooksd), y = .cooksd)) +
geom_bar(stat = "identity", fill = "steelblue") +
geom_hline(yintercept = 4 / nrow(influence_m_imp), color = "red", linetype = "dashed") +
labs(x = "Observation", y = "Cook's Distance", title = "Influential Points (Cook's Distance)") +
theme_minimal()
influence_m_imp <- influence_m_imp %>%
mutate(outlier = ifelse(abs(.std.resid) > 2, TRUE, FALSE))
# Cook's distance plot
ggplot(influence_m_imp, aes(x = .hat, y = .cooksd)) +
geom_point() +
labs(x = "Leverage", y = "Cook's Distance")
# Scatter plot with outliers highlighted
ggplot(influence_m_imp, aes(x = fitted_imputed1 , y = residual_imputed1, color = outlier)) +
geom_point(size = 2) +
geom_hline(yintercept = 0, linetype = "dashed") +
labs(x = "Fitted Values", y = "Standardized Residuals",
title = "Scatter Plot for Outlier Detection") +
scale_color_manual(values = c("black", "red")) +
theme_minimal()
### Transform Response & Goodness-of-Fit ###
# Numeric response
Imputed_data1$Diabetes_num <- ifelse(Imputed_data1$Diabetes == "Yes", 1, 0)
# Hosmer-Lemeshow test
library(ResourceSelection)
hoslem.test(Imputed_data1$Diabetes_num, fitted(m_imp))
Hosmer and Lemeshow goodness of fit (GOF) test
data: Imputed_data1$Diabetes_num, fitted(m_imp)
X-squared = 12190, df = 8, p-value < 2.2e-16# ANOVA for model
anova(m_imp)Analysis of Deviance Table
Model: binomial, link: logit
Response: Diabetes
Terms added sequentially (first to last)
Df Deviance Resid. Df Resid. Dev Pr(>Chi)
NULL 9812 11267
Age 1 31.1696 9811 11236 2.364e-08 ***
Gender 1 3.9271 9810 11232 0.04751 *
Race 4 12.2392 9806 11220 0.01566 *
BMI 3 0.7227 9803 11219 0.86784
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(m_imp, test = "Chisq")Analysis of Deviance Table
Model: binomial, link: logit
Response: Diabetes
Terms added sequentially (first to last)
Df Deviance Resid. Df Resid. Dev Pr(>Chi)
NULL 9812 11267
Age 1 31.1696 9811 11236 2.364e-08 ***
Gender 1 3.9271 9810 11232 0.04751 *
Race 4 12.2392 9806 11220 0.01566 *
BMI 3 0.7227 9803 11219 0.86784
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Adjusted R²
library(glmtoolbox)
adjR2(m_imp)[1] 0.00335# Residual vs fitted for imputed data
plot(m_imp$fitted.values, resid(m_imp),
xlab = "Fitted values",
ylab = "Residuals",
main = "Residuals vs Fitted",
pch = 19, col = "blue")
abline(h = 0, col = "red", lwd = 2)
# Frequentist logistic regression on raw datd - Firth logistic regression (penalized regression)
library(logistf)
m_firth <- logistf(Diabetes ~ BMI + Age + Gender + Race,
data = merged_data)
summary(m_firth)logistf(formula = Diabetes ~ BMI + Age + Gender + Race, data = merged_data)
Model fitted by Penalized ML
Coefficients:
coef se(coef) lower 0.95
(Intercept) 2.2909182 2.0520292 -1.7059362
BMIOverweight 2.5151817 1.9838969 -1.2146946
BMIObese -0.4519637 1.8778881 -5.4843160
Age -0.2545704 0.1911568 -1.4579663
GenderFemale -2.1675543 2.0190594 -17.0316807
RaceOther Hispanic 0.9518795 1.9225099 -12.0194116
RaceNon-Hispanic White -2.5671499 2.2189914 -21.6563170
RaceNon-Hispanic Black 3.3280688 2.2005227 -0.4678607
RaceOther Race - Including Multi-Racial 1.3072954 2.0609641 -2.6483209
upper 0.95 Chisq p method
(Intercept) 16.1697706 1.25690744 0.26223728 2
BMIOverweight 21.9385716 1.68994830 0.19360778 2
BMIObese 3.4687761 0.05538832 0.81393924 2
Age 0.1148471 1.81192970 0.17827692 2
GenderFemale 5.9967059 0.69556457 0.40427809 2
RaceOther Hispanic 10.8617660 0.20246895 0.65273532 2
RaceNon-Hispanic White 1.6700624 1.36287409 0.24304000 2
RaceNon-Hispanic Black 25.3575974 2.86971841 0.09026066 2
RaceOther Race - Including Multi-Racial 21.7820826 0.39215076 0.53117103 2
Method: 1-Wald, 2-Profile penalized log-likelihood, 3-None
Likelihood ratio test=6.066658 on 8 df, p=0.6397653, n=14
Wald test = 5.257182 on 8 df, p = 0.7297677# Imputed data plots ## pred_prob_imputed #
Imputed_data1 <- Imputed_data1 %>%
mutate(pred_prob_imputed = predict(m_imp, type = "response")) # predicted probabilities
Imputed_plot <- Imputed_data1 %>% select(BMI, pred_prob_imputed) %>% mutate(Source = "Imputed")
# Rename probability column to common name
Imputed_plot <- Imputed_plot %>% rename(Pred_Prob = pred_prob_imputed)
ggplot(Imputed_data1, aes(x = BMI, y = pred_prob_imputed, fill = BMI)) +
geom_boxplot(alpha = 0.6) +
geom_jitter(width = 0.2, alpha = 0.3, size = 1) +
labs(x = "BMI Category", y = "Predicted Probability of Diabetes",
title = "Predicted Diabetes Probability by BMI Category") +
theme_minimal()
merged_data_clean <- merged_data %>%
filter(!is.na(BMI), !is.na(Diabetes))
ggplot(merged_data_clean, aes(x = BMI, fill = factor(Diabetes))) +
geom_bar(position = "fill") +
scale_y_continuous(labels = scales::percent) +
labs(x = "BMI Category", y = "Proportion with Diabetes",
fill = "Diabetes",
title = "Observed Diabetes Proportions by BMI Category") +
theme_minimal()
ggplot(Imputed_data1, aes(x = Race, y = pred_prob_imputed, fill = Race)) +
geom_boxplot(alpha = 0.6) +
geom_jitter(width = 0.2, alpha = 0.3, size = 1) +
labs(x = "Race ", y = "Predicted Probability of Diabetes",
title = "Predicted Diabetes Probability by Race ") +
theme_minimal()
merged_data_clean_Race <- merged_data %>%
filter(!is.na(Race), !is.na(Diabetes))
ggplot(merged_data_clean, aes(x = Race, fill = factor(Diabetes))) +
geom_bar(position = "fill") +
scale_y_continuous(labels = scales::percent) +
labs(x = "Race ", y = "Proportion with Diabetes",
fill = "Diabetes",
title = "Observed Diabetes Proportions by Race ") +
theme_minimal()
ggplot(merged_data, aes(x = Age, y = Diabetes)) +
geom_jitter(size = 0.2)
ggplot(Imputed_data1, aes(x = Age, y = Diabetes)) +
geom_jitter(size = 0.2)
# Create age groups
# Create contingency table with Diabetes
Imputed_data1$Age_group <- ifelse(Imputed_data1$Age < 40, "<40", ">=40")
tab_age <- table(Imputed_data1$Age_group, Imputed_data1$Diabetes)
prop_age <- prop.table(tab_age, 1) * 100
tab_age
Yes No
<40 4414 1691
>=40 2838 870prop_age
Yes No
<40 72.30139 27.69861
>=40 76.53722 23.46278# Convert table to data frame
df_age <- as.data.frame(tab_age)
names(df_age) <- c("Age_group", "Diabetes", "Count") # rename columns## Reference: Gelman et al., 2008, “Weakly informative priors: Normal(0, 2.5) for coefficients (b) and Normal(0, 5) for the intercept as default weakly informative priors for logistic regression ##
library(brms)
priors <- c(
set_prior("normal(0, 2.5)", class = "b"), # for coefficients
set_prior("normal(0, 5)", class = "Intercept") # for intercept
)
formula_bayes <- bf(Diabetes ~ Age + BMI + Gender + Race)
Diabetes_prior <- brm(
formula = formula_bayes,
data = Imputed_data1,
family = bernoulli(link = "logit"), # logistic regression
prior = priors,
chains = 4,
iter = 2000,
seed = 123,
control = list(adapt_delta = 0.95)
)Running /opt/R/4.4.2/lib/R/bin/R CMD SHLIB foo.c
using C compiler: ‘gcc (GCC) 11.5.0 20240719 (Red Hat 11.5.0-2)’
gcc -I"/opt/R/4.4.2/lib/R/include" -DNDEBUG -I"/opt/R/4.4.2/lib/R/library/Rcpp/include/" -I"/opt/R/4.4.2/lib/R/library/RcppEigen/include/" -I"/opt/R/4.4.2/lib/R/library/RcppEigen/include/unsupported" -I"/opt/R/4.4.2/lib/R/library/BH/include" -I"/opt/R/4.4.2/lib/R/library/StanHeaders/include/src/" -I"/opt/R/4.4.2/lib/R/library/StanHeaders/include/" -I"/opt/R/4.4.2/lib/R/library/RcppParallel/include/" -I"/opt/R/4.4.2/lib/R/library/rstan/include" -DEIGEN_NO_DEBUG -DBOOST_DISABLE_ASSERTS -DBOOST_PENDING_INTEGER_LOG2_HPP -DSTAN_THREADS -DUSE_STANC3 -DSTRICT_R_HEADERS -DBOOST_PHOENIX_NO_VARIADIC_EXPRESSION -D_HAS_AUTO_PTR_ETC=0 -include '/opt/R/4.4.2/lib/R/library/StanHeaders/include/stan/math/prim/fun/Eigen.hpp' -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1 -I/usr/local/include -fpic -g -O2 -c foo.c -o foo.o
In file included from /opt/R/4.4.2/lib/R/library/RcppEigen/include/Eigen/Core:19,
from /opt/R/4.4.2/lib/R/library/RcppEigen/include/Eigen/Dense:1,
from /opt/R/4.4.2/lib/R/library/StanHeaders/include/stan/math/prim/fun/Eigen.hpp:22,
from <command-line>:
/opt/R/4.4.2/lib/R/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:679:10: fatal error: cmath: No such file or directory
679 | #include <cmath>
| ^~~~~~~
compilation terminated.
make: *** [/opt/R/4.4.2/lib/R/etc/Makeconf:195: foo.o] Error 1
SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 0.000475 seconds
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Chain 1: Adjust your expectations accordingly!
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Chain 1:
SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
Chain 2:
Chain 2: Gradient evaluation took 0.000586 seconds
Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 5.86 seconds.
Chain 2: Adjust your expectations accordingly!
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Chain 2:
Chain 2: Elapsed Time: 18.047 seconds (Warm-up)
Chain 2: 6 seconds (Sampling)
Chain 2: 24.047 seconds (Total)
Chain 2:
SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
Chain 3:
Chain 3: Gradient evaluation took 0.000501 seconds
Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 5.01 seconds.
Chain 3: Adjust your expectations accordingly!
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Chain 3:
Chain 3: Elapsed Time: 10.629 seconds (Warm-up)
Chain 3: 6.759 seconds (Sampling)
Chain 3: 17.388 seconds (Total)
Chain 3:
SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
Chain 4:
Chain 4: Gradient evaluation took 0.000379 seconds
Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 3.79 seconds.
Chain 4: Adjust your expectations accordingly!
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Chain 4:
Chain 4: Elapsed Time: 15.723 seconds (Warm-up)
Chain 4: 8.353 seconds (Sampling)
Chain 4: 24.076 seconds (Total)
Chain 4: summary(Diabetes_prior) Family: bernoulli
Links: mu = logit
Formula: Diabetes ~ Age + BMI + Gender + Race
Data: Imputed_data1 (Number of observations: 9813)
Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
total post-warmup draws = 4000
Regression Coefficients:
Estimate Est.Error l-95% CI u-95% CI Rhat
Intercept -0.77 0.16 -1.09 -0.44 1.00
Age -0.00 0.00 -0.01 -0.00 1.00
BMINormalweight 0.03 0.16 -0.27 0.34 1.00
BMIOverweight 0.08 0.16 -0.24 0.40 1.00
BMIObese 0.03 0.16 -0.29 0.35 1.00
GenderFemale -0.09 0.05 -0.18 -0.00 1.00
RaceOtherHispanic -0.15 0.09 -0.34 0.03 1.00
RaceNonMHispanicWhite -0.21 0.07 -0.34 -0.08 1.00
RaceNonMHispanicBlack -0.06 0.07 -0.20 0.09 1.00
RaceOtherRaceMIncludingMultiMRacial -0.11 0.08 -0.26 0.05 1.00
Bulk_ESS Tail_ESS
Intercept 1834 1902
Age 4495 2737
BMINormalweight 1820 1960
BMIOverweight 1899 2263
BMIObese 1884 1952
GenderFemale 3564 2756
RaceOtherHispanic 2685 2528
RaceNonMHispanicWhite 2156 2917
RaceNonMHispanicBlack 2444 2590
RaceOtherRaceMIncludingMultiMRacial 2311 2896
Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).plot(Diabetes_prior) 
posterior_predict(Diabetes_prior) [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
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[,1133] [,1134] [,1135] [,1136] [,1137] [,1138] [,1139] [,1140] [,1141]
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[,1142] [,1143] [,1144] [,1145] [,1146] [,1147] [,1148] [,1149] [,1150]
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[,1151] [,1152] [,1153] [,1154] [,1155] [,1156] [,1157] [,1158] [,1159]
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[,1160] [,1161] [,1162] [,1163] [,1164] [,1165] [,1166] [,1167] [,1168]
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[,1169] [,1170] [,1171] [,1172] [,1173] [,1174] [,1175] [,1176] [,1177]
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[,1178] [,1179] [,1180] [,1181] [,1182] [,1183] [,1184] [,1185] [,1186]
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[,1187] [,1188] [,1189] [,1190] [,1191] [,1192] [,1193] [,1194] [,1195]
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[,1196] [,1197] [,1198] [,1199] [,1200] [,1201] [,1202] [,1203] [,1204]
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[,1205] [,1206] [,1207] [,1208] [,1209] [,1210] [,1211] [,1212] [,1213]
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[,1214] [,1215] [,1216] [,1217] [,1218] [,1219] [,1220] [,1221] [,1222]
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[,1223] [,1224] [,1225] [,1226] [,1227] [,1228] [,1229] [,1230] [,1231]
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[,1232] [,1233] [,1234] [,1235] [,1236] [,1237] [,1238] [,1239] [,1240]
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[,1241] [,1242] [,1243] [,1244] [,1245] [,1246] [,1247] [,1248] [,1249]
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[,1250] [,1251] [,1252] [,1253] [,1254] [,1255] [,1256] [,1257] [,1258]
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[,1259] [,1260] [,1261] [,1262] [,1263] [,1264] [,1265] [,1266] [,1267]
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[,1268] [,1269] [,1270] [,1271] [,1272] [,1273] [,1274] [,1275] [,1276]
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[,1277] [,1278] [,1279] [,1280] [,1281] [,1282] [,1283] [,1284] [,1285]
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[,1286] [,1287] [,1288] [,1289] [,1290] [,1291] [,1292] [,1293] [,1294]
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[,1295] [,1296] [,1297] [,1298] [,1299] [,1300] [,1301] [,1302] [,1303]
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[,1304] [,1305] [,1306] [,1307] [,1308] [,1309] [,1310] [,1311] [,1312]
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[,1313] [,1314] [,1315] [,1316] [,1317] [,1318] [,1319] [,1320] [,1321]
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[,1322] [,1323] [,1324] [,1325] [,1326] [,1327] [,1328] [,1329] [,1330]
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[,1331] [,1332] [,1333] [,1334] [,1335] [,1336] [,1337] [,1338] [,1339]
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[,1340] [,1341] [,1342] [,1343] [,1344] [,1345] [,1346] [,1347] [,1348]
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[,1349] [,1350] [,1351] [,1352] [,1353] [,1354] [,1355] [,1356] [,1357]
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[,1358] [,1359] [,1360] [,1361] [,1362] [,1363] [,1364] [,1365] [,1366]
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[,1367] [,1368] [,1369] [,1370] [,1371] [,1372] [,1373] [,1374] [,1375]
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[,1376] [,1377] [,1378] [,1379] [,1380] [,1381] [,1382] [,1383] [,1384]
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[,1385] [,1386] [,1387] [,1388] [,1389] [,1390] [,1391] [,1392] [,1393]
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[,1394] [,1395] [,1396] [,1397] [,1398] [,1399] [,1400] [,1401] [,1402]
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[,1403] [,1404] [,1405] [,1406] [,1407] [,1408] [,1409] [,1410] [,1411]
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[,1412] [,1413] [,1414] [,1415] [,1416] [,1417] [,1418] [,1419] [,1420]
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[,1421] [,1422] [,1423] [,1424] [,1425] [,1426] [,1427] [,1428] [,1429]
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[,1430] [,1431] [,1432] [,1433] [,1434] [,1435] [,1436] [,1437] [,1438]
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[,1439] [,1440] [,1441] [,1442] [,1443] [,1444] [,1445] [,1446] [,1447]
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[,1448] [,1449] [,1450] [,1451] [,1452] [,1453] [,1454] [,1455] [,1456]
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[,1457] [,1458] [,1459] [,1460] [,1461] [,1462] [,1463] [,1464] [,1465]
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[,1466] [,1467] [,1468] [,1469] [,1470] [,1471] [,1472] [,1473] [,1474]
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[,1475] [,1476] [,1477] [,1478] [,1479] [,1480] [,1481] [,1482] [,1483]
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[,1484] [,1485] [,1486] [,1487] [,1488] [,1489] [,1490] [,1491] [,1492]
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[,1493] [,1494] [,1495] [,1496] [,1497] [,1498] [,1499] [,1500] [,1501]
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[,1502] [,1503] [,1504] [,1505] [,1506] [,1507] [,1508] [,1509] [,1510]
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[,1511] [,1512] [,1513] [,1514] [,1515] [,1516] [,1517] [,1518] [,1519]
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[,1520] [,1521] [,1522] [,1523] [,1524] [,1525] [,1526] [,1527] [,1528]
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[,1529] [,1530] [,1531] [,1532] [,1533] [,1534] [,1535] [,1536] [,1537]
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[,1538] [,1539] [,1540] [,1541] [,1542] [,1543] [,1544] [,1545] [,1546]
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[,1547] [,1548] [,1549] [,1550] [,1551] [,1552] [,1553] [,1554] [,1555]
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[,1556] [,1557] [,1558] [,1559] [,1560] [,1561] [,1562] [,1563] [,1564]
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[,1565] [,1566] [,1567] [,1568] [,1569] [,1570] [,1571] [,1572] [,1573]
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[,1574] [,1575] [,1576] [,1577] [,1578] [,1579] [,1580] [,1581] [,1582]
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[,1583] [,1584] [,1585] [,1586] [,1587] [,1588] [,1589] [,1590] [,1591]
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[,1592] [,1593] [,1594] [,1595] [,1596] [,1597] [,1598] [,1599] [,1600]
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[,1601] [,1602] [,1603] [,1604] [,1605] [,1606] [,1607] [,1608] [,1609]
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[,1610] [,1611] [,1612] [,1613] [,1614] [,1615] [,1616] [,1617] [,1618]
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[,1619] [,1620] [,1621] [,1622] [,1623] [,1624] [,1625] [,1626] [,1627]
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[,1628] [,1629] [,1630] [,1631] [,1632] [,1633] [,1634] [,1635] [,1636]
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[,1637] [,1638] [,1639] [,1640] [,1641] [,1642] [,1643] [,1644] [,1645]
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[,1646] [,1647] [,1648] [,1649] [,1650] [,1651] [,1652] [,1653] [,1654]
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[,1655] [,1656] [,1657] [,1658] [,1659] [,1660] [,1661] [,1662] [,1663]
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[,1664] [,1665] [,1666] [,1667] [,1668] [,1669] [,1670] [,1671] [,1672]
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[,1673] [,1674] [,1675] [,1676] [,1677] [,1678] [,1679] [,1680] [,1681]
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[,1682] [,1683] [,1684] [,1685] [,1686] [,1687] [,1688] [,1689] [,1690]
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[,1691] [,1692] [,1693] [,1694] [,1695] [,1696] [,1697] [,1698] [,1699]
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[,1700] [,1701] [,1702] [,1703] [,1704] [,1705] [,1706] [,1707] [,1708]
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[,1709] [,1710] [,1711] [,1712] [,1713] [,1714] [,1715] [,1716] [,1717]
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[,1718] [,1719] [,1720] [,1721] [,1722] [,1723] [,1724] [,1725] [,1726]
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[,1727] [,1728] [,1729] [,1730] [,1731] [,1732] [,1733] [,1734] [,1735]
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[,1736] [,1737] [,1738] [,1739] [,1740] [,1741] [,1742] [,1743] [,1744]
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[,1745] [,1746] [,1747] [,1748] [,1749] [,1750] [,1751] [,1752] [,1753]
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[,1754] [,1755] [,1756] [,1757] [,1758] [,1759] [,1760] [,1761] [,1762]
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[,1763] [,1764] [,1765] [,1766] [,1767] [,1768] [,1769] [,1770] [,1771]
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[,1772] [,1773] [,1774] [,1775] [,1776] [,1777] [,1778] [,1779] [,1780]
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[,1781] [,1782] [,1783] [,1784] [,1785] [,1786] [,1787] [,1788] [,1789]
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[,1790] [,1791] [,1792] [,1793] [,1794] [,1795] [,1796] [,1797] [,1798]
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[,1799] [,1800] [,1801] [,1802] [,1803] [,1804] [,1805] [,1806] [,1807]
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[,1808] [,1809] [,1810] [,1811] [,1812] [,1813] [,1814] [,1815] [,1816]
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[,1817] [,1818] [,1819] [,1820] [,1821] [,1822] [,1823] [,1824] [,1825]
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[,1826] [,1827] [,1828] [,1829] [,1830] [,1831] [,1832] [,1833] [,1834]
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[,1835] [,1836] [,1837] [,1838] [,1839] [,1840] [,1841] [,1842] [,1843]
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[,1844] [,1845] [,1846] [,1847] [,1848] [,1849] [,1850] [,1851] [,1852]
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[,1853] [,1854] [,1855] [,1856] [,1857] [,1858] [,1859] [,1860] [,1861]
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[,1862] [,1863] [,1864] [,1865] [,1866] [,1867] [,1868] [,1869] [,1870]
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[,1871] [,1872] [,1873] [,1874] [,1875] [,1876] [,1877] [,1878] [,1879]
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[,1880] [,1881] [,1882] [,1883] [,1884] [,1885] [,1886] [,1887] [,1888]
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[,1889] [,1890] [,1891] [,1892] [,1893] [,1894] [,1895] [,1896] [,1897]
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[,1898] [,1899] [,1900] [,1901] [,1902] [,1903] [,1904] [,1905] [,1906]
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[,1907] [,1908] [,1909] [,1910] [,1911] [,1912] [,1913] [,1914] [,1915]
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[,1916] [,1917] [,1918] [,1919] [,1920] [,1921] [,1922] [,1923] [,1924]
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[,1925] [,1926] [,1927] [,1928] [,1929] [,1930] [,1931] [,1932] [,1933]
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[,1934] [,1935] [,1936] [,1937] [,1938] [,1939] [,1940] [,1941] [,1942]
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[,1943] [,1944] [,1945] [,1946] [,1947] [,1948] [,1949] [,1950] [,1951]
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[,1952] [,1953] [,1954] [,1955] [,1956] [,1957] [,1958] [,1959] [,1960]
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[,1961] [,1962] [,1963] [,1964] [,1965] [,1966] [,1967] [,1968] [,1969]
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[,1970] [,1971] [,1972] [,1973] [,1974] [,1975] [,1976] [,1977] [,1978]
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[,1979] [,1980] [,1981] [,1982] [,1983] [,1984] [,1985] [,1986] [,1987]
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[,1988] [,1989] [,1990] [,1991] [,1992] [,1993] [,1994] [,1995] [,1996]
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[,1997] [,1998] [,1999] [,2000] [,2001] [,2002] [,2003] [,2004] [,2005]
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[,2006] [,2007] [,2008] [,2009] [,2010] [,2011] [,2012] [,2013] [,2014]
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[,2015] [,2016] [,2017] [,2018] [,2019] [,2020] [,2021] [,2022] [,2023]
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[,2024] [,2025] [,2026] [,2027] [,2028] [,2029] [,2030] [,2031] [,2032]
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[,2033] [,2034] [,2035] [,2036] [,2037] [,2038] [,2039] [,2040] [,2041]
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[,2042] [,2043] [,2044] [,2045] [,2046] [,2047] [,2048] [,2049] [,2050]
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[,2051] [,2052] [,2053] [,2054] [,2055] [,2056] [,2057] [,2058] [,2059]
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[,2060] [,2061] [,2062] [,2063] [,2064] [,2065] [,2066] [,2067] [,2068]
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[,2069] [,2070] [,2071] [,2072] [,2073] [,2074] [,2075] [,2076] [,2077]
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[,2078] [,2079] [,2080] [,2081] [,2082] [,2083] [,2084] [,2085] [,2086]
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[,2087] [,2088] [,2089] [,2090] [,2091] [,2092] [,2093] [,2094] [,2095]
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[,2096] [,2097] [,2098] [,2099] [,2100] [,2101] [,2102] [,2103] [,2104]
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[,2105] [,2106] [,2107] [,2108] [,2109] [,2110] [,2111] [,2112] [,2113]
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[,2114] [,2115] [,2116] [,2117] [,2118] [,2119] [,2120] [,2121] [,2122]
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[,2123] [,2124] [,2125] [,2126] [,2127] [,2128] [,2129] [,2130] [,2131]
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[,2132] [,2133] [,2134] [,2135] [,2136] [,2137] [,2138] [,2139] [,2140]
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[,2141] [,2142] [,2143] [,2144] [,2145] [,2146] [,2147] [,2148] [,2149]
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[,2150] [,2151] [,2152] [,2153] [,2154] [,2155] [,2156] [,2157] [,2158]
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[,2159] [,2160] [,2161] [,2162] [,2163] [,2164] [,2165] [,2166] [,2167]
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[,2168] [,2169] [,2170] [,2171] [,2172] [,2173] [,2174] [,2175] [,2176]
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[,2177] [,2178] [,2179] [,2180] [,2181] [,2182] [,2183] [,2184] [,2185]
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[,2186] [,2187] [,2188] [,2189] [,2190] [,2191] [,2192] [,2193] [,2194]
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[,2195] [,2196] [,2197] [,2198] [,2199] [,2200] [,2201] [,2202] [,2203]
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[,2204] [,2205] [,2206] [,2207] [,2208] [,2209] [,2210] [,2211] [,2212]
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[,2213] [,2214] [,2215] [,2216] [,2217] [,2218] [,2219] [,2220] [,2221]
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[,2222] [,2223] [,2224] [,2225] [,2226] [,2227] [,2228] [,2229] [,2230]
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[,2231] [,2232] [,2233] [,2234] [,2235] [,2236] [,2237] [,2238] [,2239]
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[,2240] [,2241] [,2242] [,2243] [,2244] [,2245] [,2246] [,2247] [,2248]
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[,2249] [,2250] [,2251] [,2252] [,2253] [,2254] [,2255] [,2256] [,2257]
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[7,] 0 0 1 1 0 0 0 0 0
[8,] 0 0 1 1 0 0 0 0 0
[9,] 1 1 0 1 1 0 0 0 0
[10,] 1 0 0 1 1 0 0 0 0
[,2258] [,2259] [,2260] [,2261] [,2262] [,2263] [,2264] [,2265] [,2266]
[1,] 1 0 1 1 0 1 0 0 0
[2,] 0 0 0 1 0 0 1 0 0
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[4,] 0 0 0 0 0 1 0 0 0
[5,] 0 0 0 0 0 0 0 0 0
[6,] 0 0 0 0 1 1 0 0 0
[7,] 0 1 0 0 0 0 0 0 0
[8,] 0 0 0 0 0 0 0 0 0
[9,] 0 0 0 1 0 0 0 0 0
[10,] 1 0 1 1 0 1 0 0 0
[,2267] [,2268] [,2269] [,2270] [,2271] [,2272] [,2273] [,2274] [,2275]
[1,] 0 0 1 0 0 0 1 0 1
[2,] 0 0 1 0 0 0 1 0 0
[3,] 0 0 0 0 0 0 0 0 0
[4,] 1 1 0 0 1 0 0 0 0
[5,] 0 0 0 0 0 0 0 0 1
[6,] 1 1 0 0 0 0 0 1 0
[7,] 0 0 0 0 0 0 0 0 0
[8,] 1 0 0 0 1 1 1 0 0
[9,] 0 0 1 1 0 0 1 1 0
[10,] 0 0 0 0 0 0 0 0 0
[,2276] [,2277] [,2278] [,2279] [,2280] [,2281] [,2282] [,2283] [,2284]
[1,] 0 0 0 0 0 1 0 0 1
[2,] 0 0 0 0 0 0 1 0 0
[3,] 0 1 0 0 1 0 0 0 0
[4,] 0 0 1 1 0 0 0 0 0
[5,] 1 1 1 1 0 0 0 0 1
[6,] 0 1 0 1 1 0 0 0 1
[7,] 0 0 0 0 0 0 0 0 0
[8,] 1 0 0 1 0 1 1 0 1
[9,] 0 0 1 0 0 0 0 0 0
[10,] 1 0 0 0 0 0 1 0 0
[,2285] [,2286] [,2287] [,2288] [,2289] [,2290] [,2291] [,2292] [,2293]
[1,] 1 1 0 0 0 1 1 1 0
[2,] 0 1 0 0 0 0 0 0 0
[3,] 1 1 0 1 0 0 0 1 0
[4,] 0 0 0 0 0 0 1 1 0
[5,] 1 0 1 1 0 0 0 0 1
[6,] 0 1 0 0 0 1 0 0 0
[7,] 1 0 1 0 0 0 1 0 0
[8,] 0 0 0 0 1 0 0 0 1
[9,] 0 1 0 0 1 1 1 1 0
[10,] 0 0 0 0 0 0 0 0 0
[,2294] [,2295] [,2296] [,2297] [,2298] [,2299] [,2300] [,2301] [,2302]
[1,] 0 0 0 0 0 0 1 0 1
[2,] 0 1 0 1 0 1 1 0 0
[3,] 1 0 0 0 0 1 0 0 1
[4,] 0 1 0 1 1 1 1 0 0
[5,] 0 0 0 0 0 0 0 1 0
[6,] 1 0 0 0 0 0 0 1 0
[7,] 1 0 0 0 0 0 1 1 0
[8,] 0 1 0 0 1 0 1 0 0
[9,] 0 0 0 0 0 0 1 1 0
[10,] 0 1 0 0 1 0 0 1 0
[,2303] [,2304] [,2305] [,2306] [,2307] [,2308] [,2309] [,2310] [,2311]
[1,] 0 1 1 0 0 1 1 1 0
[2,] 1 1 1 0 1 0 1 0 0
[3,] 1 0 1 0 0 1 0 0 1
[4,] 0 0 0 0 0 1 0 0 1
[5,] 1 0 0 1 0 1 0 0 0
[6,] 0 0 0 0 0 1 0 0 0
[7,] 0 1 1 1 1 0 0 0 0
[8,] 0 1 0 1 1 1 0 0 0
[9,] 0 1 0 1 0 0 0 0 0
[10,] 0 0 0 0 1 0 0 0 0
[,2312] [,2313] [,2314] [,2315] [,2316] [,2317] [,2318] [,2319] [,2320]
[1,] 0 0 1 0 0 0 0 0 0
[2,] 0 0 1 0 1 0 1 0 1
[3,] 0 1 1 0 0 0 0 0 1
[4,] 1 0 0 0 1 1 0 1 0
[5,] 1 0 0 0 0 0 0 0 0
[6,] 0 1 0 0 0 0 0 1 0
[7,] 0 0 0 0 0 0 0 1 0
[8,] 0 0 0 0 1 0 0 1 0
[9,] 0 0 1 0 0 1 0 1 1
[10,] 0 0 0 0 0 0 0 0 0
[,2321] [,2322] [,2323] [,2324] [,2325] [,2326] [,2327] [,2328] [,2329]
[1,] 0 0 1 1 0 0 0 0 0
[2,] 0 0 0 1 0 0 0 0 0
[3,] 0 1 0 0 0 0 0 0 1
[4,] 0 0 0 0 0 1 0 0 0
[5,] 1 1 0 0 1 0 0 0 0
[6,] 0 0 0 1 0 1 1 1 0
[7,] 0 0 0 1 0 1 0 0 0
[8,] 0 0 0 0 0 1 1 0 0
[9,] 1 0 0 0 0 0 0 1 0
[10,] 0 0 0 1 1 1 1 1 0
[,2330] [,2331] [,2332] [,2333] [,2334] [,2335] [,2336] [,2337] [,2338]
[1,] 1 0 0 0 0 0 0 0 0
[2,] 1 0 0 1 1 0 0 0 0
[3,] 0 1 0 0 0 0 1 1 0
[4,] 1 0 0 0 1 0 1 0 0
[5,] 0 0 0 0 0 1 0 0 0
[6,] 0 0 0 0 0 0 1 1 1
[7,] 0 1 0 1 0 0 1 0 0
[8,] 0 1 1 0 1 0 0 1 0
[9,] 1 1 0 0 1 1 0 0 0
[10,] 1 0 0 1 0 1 1 0 0
[,2339] [,2340] [,2341] [,2342] [,2343] [,2344] [,2345] [,2346] [,2347]
[1,] 0 0 0 0 0 0 0 0 1
[2,] 1 1 0 0 1 0 0 0 0
[3,] 0 0 1 0 1 0 0 0 1
[4,] 0 0 0 1 0 0 0 0 1
[5,] 1 0 0 1 0 0 0 1 1
[6,] 0 0 0 0 0 1 0 0 0
[7,] 0 1 1 0 0 0 1 0 0
[8,] 0 1 1 0 1 0 0 0 0
[9,] 0 0 0 1 0 1 0 0 0
[10,] 0 0 1 0 0 0 0 0 1
[,2348] [,2349] [,2350] [,2351] [,2352] [,2353] [,2354] [,2355] [,2356]
[1,] 0 0 0 0 0 0 0 1 0
[2,] 0 0 1 0 0 1 0 0 1
[3,] 0 0 0 1 0 0 0 0 0
[4,] 0 0 0 0 0 0 0 0 0
[5,] 1 0 1 0 1 0 1 0 0
[6,] 0 0 0 0 0 0 0 1 0
[7,] 1 0 0 0 0 0 0 1 0
[8,] 0 0 1 0 0 0 0 0 0
[9,] 0 1 0 0 0 0 0 1 1
[10,] 1 1 1 0 0 0 0 0 1
[,2357] [,2358] [,2359] [,2360] [,2361] [,2362] [,2363] [,2364] [,2365]
[1,] 0 0 0 1 0 0 1 1 0
[2,] 0 1 1 0 0 1 1 0 1
[3,] 0 0 1 0 0 0 1 1 0
[4,] 1 0 0 0 1 1 0 1 1
[5,] 1 0 0 0 1 0 0 0 1
[6,] 0 0 0 1 1 0 0 0 1
[7,] 1 1 0 0 0 0 0 0 0
[8,] 1 1 0 0 0 0 0 1 0
[9,] 1 0 0 1 0 0 0 0 0
[10,] 0 0 0 0 0 0 0 1 1
[,2366] [,2367] [,2368] [,2369] [,2370] [,2371] [,2372] [,2373] [,2374]
[1,] 0 0 0 1 0 1 0 1 0
[2,] 0 0 0 0 0 1 0 0 0
[3,] 0 0 0 1 0 0 1 0 0
[4,] 0 0 0 0 1 1 1 0 1
[5,] 0 0 1 0 0 0 1 0 0
[6,] 1 0 1 0 0 0 0 1 0
[7,] 1 0 0 0 0 0 1 1 0
[8,] 0 0 0 1 0 0 0 0 0
[9,] 0 0 0 1 0 0 0 1 0
[10,] 0 0 0 0 1 0 1 0 0
[,2375] [,2376] [,2377] [,2378] [,2379] [,2380] [,2381] [,2382] [,2383]
[1,] 0 0 0 0 0 0 1 0 0
[2,] 1 1 0 0 1 0 0 1 1
[3,] 1 0 0 0 0 0 1 0 1
[4,] 0 0 0 0 0 0 0 1 0
[5,] 0 1 1 0 0 1 0 0 1
[6,] 0 1 0 0 0 0 1 0 0
[7,] 0 0 1 0 1 0 1 0 0
[8,] 0 1 1 0 0 1 1 0 0
[9,] 0 0 0 1 0 1 0 0 0
[10,] 1 0 0 0 1 1 0 0 0
[,2384] [,2385] [,2386] [,2387] [,2388] [,2389] [,2390] [,2391] [,2392]
[1,] 1 0 1 1 0 0 1 0 1
[2,] 0 0 0 0 0 1 1 0 0
[3,] 1 0 1 0 1 0 0 0 1
[4,] 0 0 0 1 0 0 1 0 0
[5,] 1 0 0 0 1 0 0 1 0
[6,] 0 0 0 0 0 0 1 0 0
[7,] 0 0 0 0 0 0 1 1 1
[8,] 0 0 1 0 0 0 0 0 0
[9,] 0 0 0 0 0 0 0 0 0
[10,] 0 0 0 0 1 1 1 0 1
[,2393] [,2394] [,2395] [,2396] [,2397] [,2398] [,2399] [,2400] [,2401]
[1,] 0 0 1 0 0 0 0 0 0
[2,] 0 0 0 1 0 0 0 0 0
[3,] 0 0 1 0 0 0 0 0 0
[4,] 0 0 0 0 1 0 0 0 1
[5,] 0 1 1 0 0 0 0 1 0
[6,] 0 1 0 0 0 0 0 0 0
[7,] 1 0 0 0 0 0 0 1 1
[8,] 1 0 0 0 0 0 0 0 0
[9,] 0 0 1 0 0 0 0 0 1
[10,] 0 0 0 0 0 1 0 0 1
[,2402] [,2403] [,2404] [,2405] [,2406] [,2407] [,2408] [,2409] [,2410]
[1,] 1 0 0 0 0 0 0 0 0
[2,] 0 1 0 0 1 0 1 0 0
[3,] 0 0 1 0 0 1 0 0 0
[4,] 0 0 0 0 0 1 0 0 0
[5,] 0 0 0 0 0 1 1 1 0
[6,] 0 1 0 1 0 0 0 0 0
[7,] 0 1 1 0 0 0 0 0 0
[8,] 0 1 0 0 1 1 1 1 0
[9,] 0 0 0 0 0 0 0 1 0
[10,] 0 0 1 1 0 0 0 1 0
[,2411] [,2412] [,2413] [,2414] [,2415] [,2416] [,2417] [,2418] [,2419]
[1,] 0 0 0 1 0 0 0 1 0
[2,] 1 1 0 0 0 0 0 0 0
[3,] 0 1 0 0 0 0 1 0 1
[4,] 1 0 1 0 0 1 1 0 0
[5,] 0 0 0 0 0 1 1 0 0
[6,] 0 0 0 1 0 1 1 0 1
[7,] 1 1 0 0 0 0 0 0 0
[8,] 0 0 0 0 0 0 0 0 0
[9,] 0 0 0 0 1 0 0 0 1
[10,] 0 0 1 0 1 0 0 1 1
[,2420] [,2421] [,2422] [,2423] [,2424] [,2425] [,2426] [,2427] [,2428]
[1,] 0 0 1 0 1 0 0 0 0
[2,] 0 1 0 0 0 0 0 0 1
[3,] 0 1 0 0 1 1 1 1 0
[4,] 0 0 0 0 0 0 0 0 1
[5,] 1 1 0 0 1 1 0 0 0
[6,] 0 0 0 0 0 1 0 0 0
[7,] 0 0 0 0 0 1 1 0 0
[8,] 0 0 0 1 0 0 1 0 1
[9,] 0 1 0 1 1 1 0 1 0
[10,] 0 1 0 0 0 0 1 0 0
[,2429] [,2430] [,2431] [,2432] [,2433] [,2434] [,2435] [,2436] [,2437]
[1,] 0 1 0 0 0 0 0 1 0
[2,] 0 1 0 0 1 0 0 0 0
[3,] 1 0 0 0 0 0 1 0 0
[4,] 1 1 1 0 1 1 1 1 0
[5,] 0 1 0 0 0 0 1 0 1
[6,] 0 0 1 1 0 1 0 0 0
[7,] 0 1 1 0 1 0 0 0 1
[8,] 1 1 1 1 0 0 0 0 0
[9,] 1 0 0 0 0 0 0 0 0
[10,] 0 0 1 0 0 1 1 0 0
[,2438] [,2439] [,2440] [,2441] [,2442] [,2443] [,2444] [,2445] [,2446]
[1,] 0 0 0 0 1 0 1 0 0
[2,] 1 0 0 0 0 1 0 0 0
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[6,] 0 0 0 1 0 0 0 0 0
[7,] 0 0 0 1 1 0 0 0 0
[8,] 0 0 1 0 0 0 0 0 0
[9,] 1 0 0 0 0 0 0 0 1
[10,] 0 0 1 0 0 0 0 1 0
[,2447] [,2448] [,2449] [,2450] [,2451] [,2452] [,2453] [,2454] [,2455]
[1,] 0 0 0 0 1 0 0 1 1
[2,] 0 0 0 0 0 1 0 0 0
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[5,] 0 0 0 0 0 0 1 0 1
[6,] 0 0 1 1 1 1 0 0 1
[7,] 0 0 1 0 0 1 0 0 0
[8,] 0 1 0 1 0 0 0 0 0
[9,] 1 0 1 0 1 0 0 0 1
[10,] 0 0 0 1 0 0 0 1 0
[,2456] [,2457] [,2458] [,2459] [,2460] [,2461] [,2462] [,2463] [,2464]
[1,] 0 1 0 1 0 1 0 0 0
[2,] 0 0 0 0 1 0 0 1 1
[3,] 0 1 1 0 0 0 0 1 0
[4,] 0 0 1 1 1 1 0 1 0
[5,] 0 0 0 0 0 0 0 1 1
[6,] 0 0 1 0 1 1 0 1 1
[7,] 0 1 1 0 0 0 1 1 0
[8,] 0 0 1 0 1 0 0 0 0
[9,] 1 0 0 0 0 0 0 0 1
[10,] 0 1 0 0 0 0 0 0 0
[,2465] [,2466] [,2467] [,2468] [,2469] [,2470] [,2471] [,2472] [,2473]
[1,] 0 0 0 0 0 0 0 0 1
[2,] 0 0 0 0 0 0 0 0 0
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[4,] 0 0 0 0 1 0 0 0 0
[5,] 0 1 0 1 0 1 0 1 0
[6,] 1 0 0 0 1 0 0 0 0
[7,] 0 0 0 1 1 0 1 0 1
[8,] 0 1 0 1 0 0 1 0 0
[9,] 0 0 0 0 0 0 1 1 1
[10,] 1 0 0 0 1 0 0 1 0
[,2474] [,2475] [,2476] [,2477] [,2478] [,2479] [,2480] [,2481] [,2482]
[1,] 1 1 1 0 0 0 1 0 1
[2,] 1 1 0 0 0 0 0 1 0
[3,] 0 0 0 0 0 0 1 0 0
[4,] 0 1 0 0 0 0 0 1 0
[5,] 0 0 0 1 0 0 1 0 0
[6,] 1 1 1 1 0 0 1 0 1
[7,] 0 0 0 0 1 0 0 0 0
[8,] 0 1 1 0 0 0 0 0 1
[9,] 1 0 0 0 1 0 1 0 0
[10,] 0 0 0 0 0 1 0 0 0
[,2483] [,2484] [,2485] [,2486] [,2487] [,2488] [,2489] [,2490] [,2491]
[1,] 1 1 0 0 0 0 1 1 0
[2,] 0 1 0 1 0 0 0 1 0
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[4,] 1 0 1 0 1 0 0 0 1
[5,] 1 0 0 1 0 0 1 0 0
[6,] 0 1 1 1 0 0 0 0 1
[7,] 0 0 1 0 0 1 0 0 0
[8,] 0 1 1 0 0 1 0 1 1
[9,] 1 0 0 0 0 0 1 0 1
[10,] 1 0 0 1 0 0 1 0 0
[,2492] [,2493] [,2494] [,2495] [,2496] [,2497] [,2498] [,2499] [,2500]
[1,] 0 1 1 0 0 0 0 0 0
[2,] 0 0 1 0 0 0 0 0 0
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[6,] 1 0 0 0 0 0 1 0 0
[7,] 1 1 0 1 0 0 0 0 0
[8,] 0 0 0 0 0 0 0 1 1
[9,] 1 0 0 0 0 1 0 1 0
[10,] 0 0 0 1 0 0 0 1 0
[,2501] [,2502] [,2503] [,2504] [,2505] [,2506] [,2507] [,2508] [,2509]
[1,] 0 0 0 0 0 0 0 0 0
[2,] 0 0 0 0 0 0 0 0 1
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[4,] 0 0 0 1 0 1 0 0 0
[5,] 0 1 0 1 0 1 0 0 0
[6,] 0 1 0 1 0 0 0 0 1
[7,] 1 1 1 1 0 1 0 0 0
[8,] 0 0 0 0 0 1 0 0 0
[9,] 0 0 0 1 0 0 1 0 0
[10,] 1 1 0 0 0 1 0 0 0
[,2510] [,2511] [,2512] [,2513] [,2514] [,2515] [,2516] [,2517] [,2518]
[1,] 0 1 0 0 0 0 0 0 0
[2,] 0 0 0 0 0 0 0 0 0
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[4,] 0 0 0 0 1 1 1 0 1
[5,] 0 0 1 0 0 0 1 1 1
[6,] 1 1 0 0 1 1 0 0 1
[7,] 0 1 0 1 0 0 1 0 0
[8,] 0 0 0 0 1 0 0 0 1
[9,] 0 0 0 0 0 0 0 0 0
[10,] 0 0 0 0 0 1 0 0 0
[,2519] [,2520] [,2521] [,2522] [,2523] [,2524] [,2525] [,2526] [,2527]
[1,] 0 1 0 0 0 0 1 1 0
[2,] 0 0 0 1 0 1 0 0 1
[3,] 0 1 0 0 1 0 1 0 0
[4,] 0 0 0 0 1 0 0 1 1
[5,] 1 0 0 0 0 0 0 0 0
[6,] 0 0 0 0 1 0 0 0 0
[7,] 0 0 0 0 1 0 0 0 0
[8,] 0 0 0 1 0 0 0 0 0
[9,] 0 0 0 0 0 0 0 0 0
[10,] 0 0 1 0 0 0 0 0 0
[,2528] [,2529] [,2530] [,2531] [,2532] [,2533] [,2534] [,2535] [,2536]
[1,] 0 0 0 1 0 0 0 0 0
[2,] 1 0 1 0 1 0 0 0 1
[3,] 0 0 1 0 0 0 1 0 0
[4,] 1 0 0 0 0 0 1 0 0
[5,] 0 1 0 1 0 1 0 0 0
[6,] 1 0 0 1 0 0 0 1 0
[7,] 0 1 0 1 0 0 1 1 0
[8,] 0 1 0 1 1 0 0 0 1
[9,] 0 0 1 1 0 0 0 0 1
[10,] 0 0 0 1 0 0 1 0 0
[,2537] [,2538] [,2539] [,2540] [,2541] [,2542] [,2543] [,2544] [,2545]
[1,] 0 0 0 0 0 0 0 0 1
[2,] 1 0 0 0 0 1 1 0 1
[3,] 0 1 0 0 0 0 0 0 0
[4,] 1 1 1 0 0 0 0 0 0
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[6,] 0 1 1 0 1 0 0 1 0
[7,] 1 0 0 0 1 0 0 1 1
[8,] 0 0 0 1 0 0 0 0 1
[9,] 1 0 0 0 0 0 0 0 0
[10,] 0 0 0 0 0 0 1 0 1
[,2546] [,2547] [,2548] [,2549] [,2550] [,2551] [,2552] [,2553] [,2554]
[1,] 1 0 0 0 1 1 0 0 1
[2,] 1 1 0 1 1 1 0 0 0
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[6,] 0 1 0 0 1 1 1 1 0
[7,] 0 0 0 0 0 0 0 0 0
[8,] 1 0 0 0 0 0 1 1 0
[9,] 1 0 0 0 0 1 0 1 0
[10,] 0 1 0 1 0 0 1 0 0
[,2555] [,2556] [,2557] [,2558] [,2559] [,2560] [,2561] [,2562] [,2563]
[1,] 0 0 0 0 0 1 0 0 0
[2,] 0 0 1 0 0 0 0 0 1
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[6,] 1 1 0 0 0 0 1 0 0
[7,] 0 0 1 0 0 0 0 0 0
[8,] 0 1 0 0 0 0 0 0 1
[9,] 0 0 0 0 0 0 0 0 0
[10,] 1 0 0 1 0 0 1 0 0
[,2564] [,2565] [,2566] [,2567] [,2568] [,2569] [,2570] [,2571] [,2572]
[1,] 0 1 0 0 0 0 0 1 1
[2,] 0 0 0 1 0 0 1 1 1
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[6,] 0 1 0 0 0 0 0 1 0
[7,] 1 0 0 1 0 0 1 1 0
[8,] 0 0 0 0 0 0 1 1 0
[9,] 1 0 0 1 0 0 0 1 0
[10,] 0 1 0 0 0 0 0 0 0
[,2573] [,2574] [,2575] [,2576] [,2577] [,2578] [,2579] [,2580] [,2581]
[1,] 0 1 1 0 0 1 0 0 1
[2,] 0 0 1 1 1 0 0 0 0
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[7,] 1 0 0 1 0 1 0 0 0
[8,] 0 0 0 1 0 1 0 1 0
[9,] 0 0 1 1 0 0 0 0 0
[10,] 0 0 1 0 0 0 1 1 0
[,2582] [,2583] [,2584] [,2585] [,2586] [,2587] [,2588] [,2589] [,2590]
[1,] 0 0 0 0 0 1 0 0 1
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[6,] 1 0 1 1 0 1 0 0 0
[7,] 0 0 1 0 0 0 1 1 1
[8,] 0 0 0 0 1 1 0 1 0
[9,] 0 0 0 0 0 1 1 0 0
[10,] 0 0 0 0 0 1 0 0 0
[,2591] [,2592] [,2593] [,2594] [,2595] [,2596] [,2597] [,2598] [,2599]
[1,] 1 0 1 1 0 1 0 0 0
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[6,] 1 0 1 1 1 0 0 1 0
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[8,] 0 0 0 1 0 0 0 0 1
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[10,] 1 0 0 0 0 0 0 1 0
[,2600] [,2601] [,2602] [,2603] [,2604] [,2605] [,2606] [,2607] [,2608]
[1,] 1 0 1 0 0 0 0 0 0
[2,] 1 0 1 0 0 0 0 1 1
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[8,] 0 0 0 0 0 1 0 0 0
[9,] 1 0 0 1 0 0 0 0 0
[10,] 0 0 0 0 0 1 0 0 0
[,2609] [,2610] [,2611] [,2612] [,2613] [,2614] [,2615] [,2616] [,2617]
[1,] 0 1 0 0 0 0 1 1 0
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[7,] 0 1 0 0 0 1 0 0 0
[8,] 1 1 1 1 0 0 0 1 0
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[10,] 0 0 0 0 0 1 0 0 0
[,2618] [,2619] [,2620] [,2621] [,2622] [,2623] [,2624] [,2625] [,2626]
[1,] 0 1 1 0 0 0 0 1 0
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[10,] 0 1 0 0 1 0 0 0 0
[,2627] [,2628] [,2629] [,2630] [,2631] [,2632] [,2633] [,2634] [,2635]
[1,] 0 0 0 0 0 0 1 0 0
[2,] 0 1 0 1 0 0 1 0 0
[3,] 1 1 0 0 0 1 0 1 1
[4,] 1 0 0 0 0 1 0 0 0
[5,] 0 1 1 0 1 0 0 0 0
[6,] 0 1 0 1 0 0 0 0 0
[7,] 1 0 0 0 0 0 0 0 0
[8,] 0 0 0 1 0 0 0 1 0
[9,] 0 0 0 1 0 0 1 0 0
[10,] 1 0 1 1 0 0 0 1 1
[,2636] [,2637] [,2638] [,2639] [,2640] [,2641] [,2642] [,2643] [,2644]
[1,] 0 0 0 0 0 0 1 0 0
[2,] 0 0 0 0 0 0 1 0 0
[3,] 0 1 0 0 0 0 0 0 0
[4,] 0 0 0 0 0 0 0 0 0
[5,] 1 0 1 1 0 1 1 0 1
[6,] 1 0 0 0 1 1 1 0 0
[7,] 0 0 0 0 0 0 1 0 0
[8,] 0 0 0 0 0 0 0 0 1
[9,] 0 1 0 0 0 0 0 0 0
[10,] 0 1 0 0 0 0 0 0 0
[,2645] [,2646] [,2647] [,2648] [,2649] [,2650] [,2651] [,2652] [,2653]
[1,] 0 1 1 0 0 0 0 1 0
[2,] 0 1 0 1 0 0 0 0 1
[3,] 1 0 0 0 0 0 0 0 0
[4,] 0 0 1 0 0 0 0 0 0
[5,] 0 0 0 1 0 0 1 0 0
[6,] 0 0 0 1 1 0 0 0 1
[7,] 0 0 0 1 0 0 1 1 1
[8,] 0 0 1 0 0 0 1 0 0
[9,] 1 0 0 0 0 1 0 0 0
[10,] 0 0 0 0 0 0 0 0 0
[,2654] [,2655] [,2656] [,2657] [,2658] [,2659] [,2660] [,2661] [,2662]
[1,] 0 0 0 0 1 1 0 1 1
[2,] 1 0 1 1 0 0 0 1 0
[3,] 0 1 1 0 0 0 1 0 1
[4,] 1 1 0 0 0 0 1 0 0
[5,] 0 0 0 0 0 0 0 0 0
[6,] 0 0 0 1 0 0 1 0 0
[7,] 0 1 1 0 1 0 0 0 1
[8,] 1 0 0 0 0 1 0 0 0
[9,] 0 0 1 0 0 1 0 0 0
[10,] 0 0 0 0 0 1 0 0 0
[,2663] [,2664] [,2665] [,2666] [,2667] [,2668] [,2669] [,2670] [,2671]
[1,] 0 0 0 0 0 0 1 0 0
[2,] 0 0 1 0 1 1 0 0 0
[3,] 0 0 0 1 0 0 0 0 0
[4,] 0 0 0 0 0 0 0 0 0
[5,] 0 0 1 0 0 0 0 0 0
[6,] 0 1 0 0 0 0 0 0 0
[7,] 0 0 0 0 0 0 1 0 1
[8,] 0 1 0 1 0 1 1 1 0
[9,] 0 0 1 0 0 1 0 0 0
[10,] 0 1 1 0 0 1 0 1 0
[,2672] [,2673] [,2674] [,2675] [,2676] [,2677] [,2678] [,2679] [,2680]
[1,] 0 0 1 0 1 1 1 0 0
[2,] 0 0 0 1 0 0 1 0 0
[3,] 1 0 1 0 0 0 0 0 0
[4,] 0 1 0 1 1 0 0 1 0
[5,] 0 0 0 0 0 1 1 0 0
[6,] 0 0 0 1 0 0 0 0 0
[7,] 0 0 1 0 0 0 0 0 1
[8,] 0 0 1 0 0 0 0 0 0
[9,] 0 0 0 0 0 0 0 0 0
[10,] 0 0 1 0 0 1 0 1 1
[,2681] [,2682] [,2683] [,2684] [,2685] [,2686] [,2687] [,2688] [,2689]
[1,] 1 0 0 1 1 0 0 1 0
[2,] 0 0 0 0 0 0 0 0 0
[3,] 0 1 0 0 1 1 0 1 1
[4,] 0 0 1 1 1 0 0 0 1
[5,] 0 1 0 1 1 0 1 0 0
[6,] 0 1 1 1 0 1 0 0 0
[7,] 0 1 0 1 0 0 1 0 1
[8,] 0 0 1 0 0 1 0 0 0
[9,] 0 0 0 0 1 0 0 0 0
[10,] 0 0 1 1 1 0 0 0 1
[,2690] [,2691] [,2692] [,2693] [,2694] [,2695] [,2696] [,2697] [,2698]
[1,] 0 0 0 0 0 0 0 0 1
[2,] 0 0 1 0 0 0 1 0 0
[3,] 0 0 1 0 0 1 1 0 1
[4,] 0 1 1 0 1 1 1 0 1
[5,] 0 0 0 0 0 0 0 0 0
[6,] 0 1 0 0 0 0 0 1 0
[7,] 1 0 0 1 1 1 0 0 0
[8,] 0 0 1 1 0 0 0 1 1
[9,] 0 0 1 0 0 0 0 0 0
[10,] 1 1 1 0 0 1 0 1 1
[,2699] [,2700] [,2701] [,2702] [,2703] [,2704] [,2705] [,2706] [,2707]
[1,] 1 0 1 0 0 0 0 0 0
[2,] 0 0 1 0 0 0 0 1 0
[3,] 0 0 0 1 0 0 0 0 0
[4,] 0 0 0 0 0 0 0 0 1
[5,] 1 1 0 0 0 0 0 0 0
[6,] 1 0 1 0 0 0 0 0 1
[7,] 0 0 0 0 1 1 1 0 0
[8,] 0 0 0 1 0 1 0 1 0
[9,] 0 0 0 0 0 0 0 1 0
[10,] 0 0 0 0 0 0 0 0 1
[,2708] [,2709] [,2710] [,2711] [,2712] [,2713] [,2714] [,2715] [,2716]
[1,] 1 0 0 1 0 0 0 0 0
[2,] 0 0 0 0 0 0 0 0 0
[3,] 0 0 0 0 0 0 1 0 1
[4,] 0 0 0 1 0 0 0 0 0
[5,] 0 0 0 1 0 0 0 0 0
[6,] 1 0 0 1 1 0 1 1 0
[7,] 0 0 1 1 0 0 1 0 0
[8,] 0 1 0 0 1 0 0 1 0
[9,] 0 0 0 1 0 0 0 0 0
[10,] 0 0 0 0 0 0 0 0 0
[,2717] [,2718] [,2719] [,2720] [,2721] [,2722] [,2723] [,2724] [,2725]
[1,] 1 0 1 1 1 0 0 1 0
[2,] 0 0 1 0 0 1 0 0 0
[3,] 1 0 0 0 0 1 0 0 0
[4,] 0 0 1 0 0 0 0 0 1
[5,] 0 1 0 0 1 1 1 1 0
[6,] 0 0 0 0 1 0 0 0 1
[7,] 0 1 0 1 1 1 1 0 1
[8,] 1 0 1 1 0 1 0 1 0
[9,] 1 1 0 1 0 1 0 1 1
[10,] 0 0 0 1 0 0 1 0 0
[,2726] [,2727] [,2728] [,2729] [,2730] [,2731] [,2732] [,2733] [,2734]
[1,] 0 0 0 0 1 0 0 0 0
[2,] 1 0 0 0 0 0 0 0 1
[3,] 0 0 1 1 1 1 0 0 0
[4,] 0 1 0 0 0 0 0 0 0
[5,] 0 0 0 1 1 0 1 0 1
[6,] 0 1 0 1 0 0 0 0 1
[7,] 0 1 1 1 0 0 0 0 0
[8,] 0 0 0 0 0 1 0 0 0
[9,] 0 0 0 0 0 0 0 0 0
[10,] 1 0 0 1 0 0 1 0 0
[,2735] [,2736] [,2737] [,2738] [,2739] [,2740] [,2741] [,2742] [,2743]
[1,] 0 1 1 1 0 1 1 0 0
[2,] 0 1 0 0 0 0 0 0 0
[3,] 0 0 0 0 0 1 0 1 1
[4,] 0 1 0 0 0 1 0 1 0
[5,] 1 0 1 0 0 0 0 0 0
[6,] 0 1 0 1 0 0 0 0 0
[7,] 1 0 0 0 1 0 0 1 0
[8,] 0 0 0 0 0 0 0 1 0
[9,] 0 0 0 0 0 0 0 0 1
[10,] 1 0 0 0 0 1 0 0 0
[,2744] [,2745] [,2746] [,2747] [,2748] [,2749] [,2750] [,2751] [,2752]
[1,] 0 0 0 1 0 0 0 0 0
[2,] 0 0 0 0 0 0 0 0 1
[3,] 0 0 1 0 0 0 0 1 1
[4,] 0 0 0 1 0 1 1 1 0
[5,] 0 0 0 0 1 0 0 0 0
[6,] 0 0 0 1 0 0 1 1 0
[7,] 1 1 0 0 0 0 1 0 0
[8,] 0 0 1 0 0 0 0 0 0
[9,] 0 1 0 1 0 0 0 0 0
[10,] 1 0 0 1 0 1 0 1 1
[,2753] [,2754] [,2755] [,2756] [,2757] [,2758] [,2759] [,2760] [,2761]
[1,] 0 0 1 0 0 0 1 0 0
[2,] 1 1 1 0 0 0 0 1 1
[3,] 0 1 1 0 1 0 0 0 1
[4,] 0 0 0 0 0 1 0 0 1
[5,] 0 0 0 1 0 0 1 0 1
[6,] 1 1 0 0 0 0 1 0 0
[7,] 0 0 0 0 0 0 0 1 0
[8,] 0 0 0 0 0 1 1 1 0
[9,] 0 1 0 1 0 0 1 1 0
[10,] 0 0 1 0 1 0 0 0 0
[,2762] [,2763] [,2764] [,2765] [,2766] [,2767] [,2768] [,2769] [,2770]
[1,] 1 1 0 0 0 0 0 0 0
[2,] 0 0 0 1 0 1 1 0 1
[3,] 0 0 0 0 1 0 0 1 0
[4,] 0 1 0 1 0 0 0 1 0
[5,] 0 1 0 0 0 0 0 0 1
[6,] 1 0 0 0 0 0 0 0 0
[7,] 0 0 0 0 0 0 1 1 0
[8,] 0 0 0 0 0 1 0 1 0
[9,] 0 0 0 0 0 1 0 0 1
[10,] 0 0 1 0 0 0 0 0 0
[,2771] [,2772] [,2773] [,2774] [,2775] [,2776] [,2777] [,2778] [,2779]
[1,] 1 0 0 1 0 0 0 0 0
[2,] 0 0 1 0 0 0 1 0 0
[3,] 0 1 0 1 0 0 1 0 0
[4,] 1 0 0 1 0 0 0 0 0
[5,] 0 0 0 0 0 0 0 1 0
[6,] 0 1 1 0 0 0 0 0 0
[7,] 1 1 0 0 1 0 1 0 0
[8,] 0 0 0 0 0 1 0 0 0
[9,] 0 1 0 1 0 1 0 1 0
[10,] 1 0 0 0 0 0 0 0 0
[,2780] [,2781] [,2782] [,2783] [,2784] [,2785] [,2786] [,2787] [,2788]
[1,] 1 0 1 0 0 0 0 0 0
[2,] 0 0 0 0 0 0 0 0 0
[3,] 1 1 1 0 1 0 1 1 1
[4,] 0 0 1 1 0 0 1 0 0
[5,] 0 1 0 0 0 0 1 1 1
[6,] 0 0 0 0 0 0 0 1 0
[7,] 0 0 0 1 1 0 0 1 0
[8,] 0 0 0 0 1 1 0 0 0
[9,] 1 0 0 0 0 1 1 0 0
[10,] 0 0 0 0 1 0 0 0 0
[,2789] [,2790] [,2791] [,2792] [,2793] [,2794] [,2795] [,2796] [,2797]
[1,] 0 0 0 0 0 1 1 0 1
[2,] 0 1 0 0 0 0 0 0 0
[3,] 0 0 0 0 0 0 0 1 0
[4,] 1 0 0 0 0 0 0 0 0
[5,] 0 0 0 0 1 1 1 0 1
[6,] 0 0 1 0 0 0 1 1 0
[7,] 1 0 0 0 0 0 1 0 0
[8,] 0 0 0 0 0 1 0 0 0
[9,] 0 1 1 0 0 1 0 0 0
[10,] 0 0 0 0 0 1 0 0 1
[,2798] [,2799] [,2800] [,2801] [,2802] [,2803] [,2804] [,2805] [,2806]
[1,] 0 0 1 1 0 0 0 0 0
[2,] 1 1 0 0 0 0 0 0 0
[3,] 0 0 0 0 0 0 0 0 1
[4,] 0 0 0 0 0 0 0 0 0
[5,] 0 0 0 0 0 0 0 0 0
[6,] 0 0 1 1 0 0 0 0 0
[7,] 1 0 0 1 0 0 0 1 0
[8,] 0 0 0 0 0 0 0 1 0
[9,] 0 0 0 0 1 0 0 0 0
[10,] 0 0 0 0 0 0 1 1 0
[,2807] [,2808] [,2809] [,2810] [,2811] [,2812] [,2813] [,2814] [,2815]
[1,] 0 0 1 1 0 0 0 1 0
[2,] 0 0 1 0 1 0 0 0 0
[3,] 0 0 0 0 0 0 0 1 0
[4,] 0 0 1 1 0 0 0 0 0
[5,] 0 0 0 1 0 0 0 0 0
[6,] 0 0 0 0 1 0 0 0 0
[7,] 0 0 0 0 0 1 0 0 0
[8,] 0 0 0 0 1 0 0 1 0
[9,] 1 1 0 1 0 0 0 0 1
[10,] 0 0 0 0 0 0 1 0 1
[,2816] [,2817] [,2818] [,2819] [,2820] [,2821] [,2822] [,2823] [,2824]
[1,] 0 0 0 0 0 0 0 0 0
[2,] 0 1 1 0 0 1 1 0 0
[3,] 0 0 0 1 0 0 0 0 1
[4,] 0 0 0 1 0 1 0 0 0
[5,] 1 1 1 0 1 0 1 0 0
[6,] 0 0 0 1 0 0 0 1 1
[7,] 0 0 1 1 0 0 0 1 1
[8,] 1 0 0 0 1 1 1 0 0
[9,] 0 0 0 1 0 0 0 0 0
[10,] 0 0 0 0 0 0 0 0 0
[,2825] [,2826] [,2827] [,2828] [,2829] [,2830] [,2831] [,2832] [,2833]
[1,] 0 0 0 0 0 0 1 1 1
[2,] 0 0 0 0 0 0 0 0 0
[3,] 1 1 0 0 1 0 0 1 1
[4,] 0 0 0 0 0 0 0 1 1
[5,] 0 0 1 1 0 0 1 0 1
[6,] 0 0 0 0 1 0 1 0 0
[7,] 0 0 1 0 0 1 0 0 0
[8,] 0 0 0 0 0 1 1 1 0
[9,] 0 0 1 1 1 0 1 1 1
[10,] 0 0 0 0 0 0 0 1 0
[,2834] [,2835] [,2836] [,2837] [,2838] [,2839] [,2840] [,2841] [,2842]
[1,] 0 0 0 0 1 1 1 1 0
[2,] 0 0 0 1 1 0 0 1 1
[3,] 0 1 0 0 1 0 1 0 0
[4,] 1 1 1 1 0 1 0 0 0
[5,] 1 0 0 0 0 0 1 0 0
[6,] 0 0 0 0 1 0 1 0 0
[7,] 0 0 0 0 0 1 0 0 0
[8,] 0 0 0 1 0 0 0 1 0
[9,] 0 1 0 0 0 0 0 0 0
[10,] 1 1 0 0 0 1 0 1 1
[,2843] [,2844] [,2845] [,2846] [,2847] [,2848] [,2849] [,2850] [,2851]
[1,] 1 1 0 0 0 1 0 0 0
[2,] 1 0 1 0 0 0 1 0 0
[3,] 0 0 0 1 1 0 0 0 1
[4,] 0 1 0 0 0 1 0 0 1
[5,] 1 0 0 0 1 1 0 0 1
[6,] 0 1 0 0 1 1 0 0 0
[7,] 0 0 0 0 1 0 0 1 0
[8,] 0 0 0 0 0 1 1 0 1
[9,] 0 0 0 1 0 0 0 0 0
[10,] 0 1 0 0 1 0 1 0 0
[,2852] [,2853] [,2854] [,2855] [,2856] [,2857] [,2858] [,2859] [,2860]
[1,] 0 0 0 0 0 0 0 0 0
[2,] 0 1 0 0 0 1 0 0 0
[3,] 0 0 0 0 0 0 1 1 0
[4,] 0 1 0 1 1 0 0 0 0
[5,] 0 1 0 0 0 1 0 0 0
[6,] 0 1 0 0 0 0 0 0 0
[7,] 1 1 1 0 0 1 0 0 0
[8,] 0 0 0 0 0 1 0 0 0
[9,] 0 0 0 1 0 0 0 1 0
[10,] 0 0 0 0 1 0 1 0 0
[,2861] [,2862] [,2863] [,2864] [,2865] [,2866] [,2867] [,2868] [,2869]
[1,] 0 0 0 0 0 0 0 1 0
[2,] 0 0 1 0 0 0 0 1 0
[3,] 0 0 0 0 0 0 0 1 0
[4,] 1 0 1 1 1 1 0 1 1
[5,] 0 0 0 0 1 0 0 0 1
[6,] 0 0 0 0 0 0 1 1 0
[7,] 0 0 0 0 0 0 0 0 0
[8,] 0 0 0 0 0 0 1 0 0
[9,] 0 0 0 1 0 0 0 0 0
[10,] 0 0 1 1 0 0 0 1 0
[,2870] [,2871] [,2872] [,2873] [,2874] [,2875] [,2876] [,2877] [,2878]
[1,] 1 1 0 0 1 0 0 0 0
[2,] 0 1 0 1 0 1 0 1 0
[3,] 0 0 0 0 0 1 0 0 1
[4,] 0 1 0 0 0 1 1 0 0
[5,] 0 0 1 0 0 0 0 0 1
[6,] 0 0 1 0 1 0 1 0 1
[7,] 0 0 0 0 0 0 0 0 1
[8,] 1 0 0 0 0 0 1 1 1
[9,] 1 0 1 0 0 0 0 1 1
[10,] 0 0 0 1 0 0 0 1 1
[,2879] [,2880] [,2881] [,2882] [,2883] [,2884] [,2885] [,2886] [,2887]
[1,] 1 1 0 0 0 0 0 1 0
[2,] 0 1 1 0 0 0 0 0 1
[3,] 0 0 1 0 0 0 1 1 0
[4,] 0 0 0 0 0 1 0 1 0
[5,] 0 0 0 1 0 0 0 0 0
[6,] 0 0 0 0 0 1 0 0 0
[7,] 0 0 1 0 1 0 0 0 0
[8,] 1 0 0 0 0 0 0 0 0
[9,] 0 0 1 0 0 0 0 0 0
[10,] 0 0 1 0 1 1 0 0 0
[,2888] [,2889] [,2890] [,2891] [,2892] [,2893] [,2894] [,2895] [,2896]
[1,] 0 1 0 0 1 0 0 1 0
[2,] 0 0 1 0 0 0 0 0 0
[3,] 0 0 0 1 0 0 0 0 0
[4,] 0 1 0 0 0 0 0 1 0
[5,] 0 0 0 0 0 1 0 1 0
[6,] 0 1 0 1 0 0 0 0 1
[7,] 0 0 0 1 0 0 0 0 0
[8,] 1 1 0 0 0 0 1 0 0
[9,] 0 0 0 0 1 0 0 0 1
[10,] 0 1 0 0 1 1 1 0 0
[,2897] [,2898] [,2899] [,2900] [,2901] [,2902] [,2903] [,2904] [,2905]
[1,] 0 0 1 1 1 0 1 0 1
[2,] 0 1 0 0 0 0 0 0 0
[3,] 0 0 1 0 0 0 1 1 0
[4,] 0 0 0 0 0 0 0 0 1
[5,] 0 0 1 1 1 1 1 0 0
[6,] 0 0 0 0 0 1 0 0 0
[7,] 1 0 1 0 0 0 1 1 0
[8,] 0 0 0 0 0 0 0 0 0
[9,] 0 0 1 0 0 0 0 0 1
[10,] 1 0 0 1 0 0 0 1 0
[,2906] [,2907] [,2908] [,2909] [,2910] [,2911] [,2912] [,2913] [,2914]
[1,] 0 0 0 0 0 1 0 0 0
[2,] 0 1 1 0 0 1 0 1 1
[3,] 0 0 0 1 0 0 0 1 0
[4,] 0 1 0 0 0 0 0 0 0
[5,] 1 0 0 0 0 1 1 0 1
[6,] 0 0 1 0 0 0 1 0 0
[7,] 1 0 0 0 0 0 0 0 0
[8,] 1 0 1 0 1 0 0 0 0
[9,] 0 0 0 0 0 0 0 0 0
[10,] 0 0 0 1 0 1 0 0 0
[,2915] [,2916] [,2917] [,2918] [,2919] [,2920] [,2921] [,2922] [,2923]
[1,] 1 0 1 0 0 0 1 0 0
[2,] 0 1 0 0 0 0 0 0 1
[3,] 0 1 0 0 0 0 0 1 0
[4,] 0 0 1 0 0 1 0 0 0
[5,] 0 0 1 1 0 1 0 0 0
[6,] 0 0 0 0 0 0 0 0 0
[7,] 0 1 0 0 0 0 0 1 0
[8,] 1 0 0 1 0 0 0 0 1
[9,] 0 0 1 0 0 1 0 1 1
[10,] 0 1 0 1 1 0 0 0 0
[,2924] [,2925] [,2926] [,2927] [,2928] [,2929] [,2930] [,2931] [,2932]
[1,] 1 1 1 0 0 0 0 0 0
[2,] 0 0 0 0 1 1 0 0 0
[3,] 1 0 0 0 1 0 0 0 1
[4,] 1 0 0 0 0 0 1 0 0
[5,] 0 0 0 0 0 0 1 0 0
[6,] 0 1 1 0 0 0 0 0 0
[7,] 0 1 1 0 0 1 0 0 0
[8,] 0 1 0 0 0 0 0 0 0
[9,] 0 0 0 0 0 0 1 0 0
[10,] 0 0 0 0 1 1 0 0 0
[,2933] [,2934] [,2935] [,2936] [,2937] [,2938] [,2939] [,2940] [,2941]
[1,] 0 1 1 1 0 0 0 1 0
[2,] 0 0 0 0 1 1 0 0 0
[3,] 0 1 0 1 1 1 1 0 0
[4,] 0 0 0 0 0 0 0 1 0
[5,] 0 1 0 1 0 0 0 1 1
[6,] 0 1 0 0 0 0 0 0 0
[7,] 0 1 0 0 1 1 0 1 0
[8,] 0 0 0 0 0 1 0 0 0
[9,] 1 0 0 0 0 0 0 0 1
[10,] 0 0 0 0 0 0 0 0 1
[,2942] [,2943] [,2944] [,2945] [,2946] [,2947] [,2948] [,2949] [,2950]
[1,] 0 1 0 0 0 0 0 0 1
[2,] 0 0 0 0 0 0 0 0 1
[3,] 0 0 0 0 0 0 0 0 0
[4,] 0 1 0 0 0 0 1 0 1
[5,] 0 0 0 1 1 0 1 1 0
[6,] 1 0 0 1 0 1 0 0 1
[7,] 1 1 0 0 0 1 0 0 0
[8,] 0 1 0 1 0 1 0 1 1
[9,] 0 1 0 0 1 0 0 0 1
[10,] 1 0 0 0 1 0 0 0 0
[,2951] [,2952] [,2953] [,2954] [,2955] [,2956] [,2957] [,2958] [,2959]
[1,] 1 1 0 0 0 1 0 0 1
[2,] 0 0 1 1 0 0 0 0 0
[3,] 0 0 1 1 1 0 0 1 0
[4,] 0 0 0 0 0 0 0 0 0
[5,] 0 0 1 0 0 0 1 0 0
[6,] 0 1 0 0 0 0 0 0 0
[7,] 0 1 0 0 1 1 0 0 0
[8,] 0 0 1 0 0 0 0 0 0
[9,] 0 0 0 0 0 0 0 0 0
[10,] 0 1 0 0 1 0 0 0 0
[,2960] [,2961] [,2962] [,2963] [,2964] [,2965] [,2966] [,2967] [,2968]
[1,] 0 0 0 0 1 0 0 0 0
[2,] 0 1 1 0 0 0 0 0 0
[3,] 0 0 1 1 0 0 1 1 0
[4,] 0 0 1 1 1 1 0 0 0
[5,] 0 0 0 1 0 1 0 0 1
[6,] 0 1 0 0 1 1 0 0 0
[7,] 0 1 0 0 0 1 0 0 0
[8,] 1 0 0 0 0 0 0 0 1
[9,] 0 1 0 0 0 1 1 0 0
[10,] 0 1 0 0 0 0 0 0 1
[,2969] [,2970] [,2971] [,2972] [,2973] [,2974] [,2975] [,2976] [,2977]
[1,] 1 0 0 1 0 0 0 0 1
[2,] 0 0 0 0 0 0 1 1 0
[3,] 0 0 0 0 1 0 0 0 0
[4,] 0 1 0 0 0 1 1 1 0
[5,] 0 0 0 1 0 0 0 0 0
[6,] 1 1 0 1 0 0 1 0 1
[7,] 0 0 0 0 0 0 1 0 1
[8,] 0 0 1 0 0 0 0 1 0
[9,] 0 0 0 0 1 0 0 0 0
[10,] 0 0 0 0 0 1 0 0 0
[,2978] [,2979] [,2980] [,2981] [,2982] [,2983] [,2984] [,2985] [,2986]
[1,] 1 0 0 0 0 1 0 0 0
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[6,] 0 0 1 1 0 0 1 0 0
[7,] 0 0 0 0 0 1 1 0 0
[8,] 0 0 0 1 0 0 0 0 0
[9,] 0 0 0 0 0 0 1 0 1
[10,] 0 1 0 0 0 0 0 0 0
[,2987] [,2988] [,2989] [,2990] [,2991] [,2992] [,2993] [,2994] [,2995]
[1,] 0 0 0 0 1 1 0 0 0
[2,] 0 0 0 0 1 1 0 0 1
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[4,] 0 0 0 0 0 0 1 1 0
[5,] 0 0 0 1 1 1 0 0 1
[6,] 0 1 0 1 0 0 0 0 1
[7,] 0 0 0 0 1 0 0 0 1
[8,] 0 1 0 0 1 0 0 0 1
[9,] 0 0 0 1 0 0 0 1 0
[10,] 0 0 0 0 0 0 0 0 0
[,2996] [,2997] [,2998] [,2999] [,3000] [,3001] [,3002] [,3003] [,3004]
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[2,] 0 0 1 1 1 0 0 0 0
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[4,] 0 0 0 0 1 1 0 0 0
[5,] 0 0 0 0 0 1 0 1 0
[6,] 0 0 1 0 0 0 0 0 1
[7,] 0 0 1 1 0 0 0 1 1
[8,] 0 1 0 1 0 0 0 0 0
[9,] 0 1 0 0 0 0 1 0 0
[10,] 1 0 0 0 0 0 0 1 1
[,3005] [,3006] [,3007] [,3008] [,3009] [,3010] [,3011] [,3012] [,3013]
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[7,] 0 1 0 0 1 0 0 0 0
[8,] 0 0 0 0 0 0 0 0 0
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[,3014] [,3015] [,3016] [,3017] [,3018] [,3019] [,3020] [,3021] [,3022]
[1,] 1 0 1 0 0 0 0 0 0
[2,] 0 0 0 0 1 0 0 0 1
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[7,] 0 1 0 0 0 0 0 1 1
[8,] 0 0 0 1 0 0 0 0 0
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[10,] 0 0 1 0 0 0 1 0 0
[,3023] [,3024] [,3025] [,3026] [,3027] [,3028] [,3029] [,3030] [,3031]
[1,] 0 0 0 1 0 1 0 0 0
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[6,] 1 0 1 1 1 0 0 0 0
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[8,] 0 0 0 0 0 0 0 0 1
[9,] 1 0 1 0 0 0 0 1 0
[10,] 0 0 0 0 0 0 1 1 0
[,3032] [,3033] [,3034] [,3035] [,3036] [,3037] [,3038] [,3039] [,3040]
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[9,] 1 1 1 0 0 0 0 0 0
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[,3041] [,3042] [,3043] [,3044] [,3045] [,3046] [,3047] [,3048] [,3049]
[1,] 0 1 0 1 1 0 0 0 0
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[,3050] [,3051] [,3052] [,3053] [,3054] [,3055] [,3056] [,3057] [,3058]
[1,] 1 1 0 0 0 1 0 0 0
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[,3059] [,3060] [,3061] [,3062] [,3063] [,3064] [,3065] [,3066] [,3067]
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[,3068] [,3069] [,3070] [,3071] [,3072] [,3073] [,3074] [,3075] [,3076]
[1,] 0 1 0 0 0 0 0 1 0
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[,3077] [,3078] [,3079] [,3080] [,3081] [,3082] [,3083] [,3084] [,3085]
[1,] 0 0 1 0 1 0 0 0 0
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[,3086] [,3087] [,3088] [,3089] [,3090] [,3091] [,3092] [,3093] [,3094]
[1,] 0 0 0 0 0 0 0 0 0
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[,3095] [,3096] [,3097] [,3098] [,3099] [,3100] [,3101] [,3102] [,3103]
[1,] 0 1 0 0 0 0 1 0 0
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[6,] 1 0 0 0 1 0 1 1 1
[7,] 0 0 1 0 0 0 0 1 0
[8,] 0 1 0 0 0 0 0 0 0
[9,] 0 0 1 0 0 0 1 1 0
[10,] 0 0 0 0 0 0 0 0 1
[,3104] [,3105] [,3106] [,3107] [,3108] [,3109] [,3110] [,3111] [,3112]
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[,3113] [,3114] [,3115] [,3116] [,3117] [,3118] [,3119] [,3120] [,3121]
[1,] 1 0 1 0 0 0 1 0 0
[2,] 0 0 0 0 1 0 0 1 0
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[8,] 0 0 0 1 1 0 0 0 1
[9,] 0 0 0 1 0 0 0 1 1
[10,] 0 0 0 1 0 0 0 0 0
[,3122] [,3123] [,3124] [,3125] [,3126] [,3127] [,3128] [,3129] [,3130]
[1,] 1 0 0 0 1 0 0 0 0
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[8,] 0 1 0 1 0 0 0 0 1
[9,] 0 1 0 1 0 0 0 0 0
[10,] 0 1 1 0 0 1 0 0 1
[,3131] [,3132] [,3133] [,3134] [,3135] [,3136] [,3137] [,3138] [,3139]
[1,] 0 0 1 0 0 0 0 0 0
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[4,] 0 1 0 0 0 1 1 1 1
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[9,] 0 0 1 1 0 1 1 0 0
[10,] 0 0 0 1 0 1 0 0 0
[,3140] [,3141] [,3142] [,3143] [,3144] [,3145] [,3146] [,3147] [,3148]
[1,] 0 0 1 1 1 0 0 1 1
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[10,] 0 0 1 0 0 0 0 0 0
[,3149] [,3150] [,3151] [,3152] [,3153] [,3154] [,3155] [,3156] [,3157]
[1,] 0 0 0 0 1 0 1 0 0
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[8,] 0 0 0 0 0 0 0 1 0
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[10,] 1 0 0 0 0 0 0 1 1
[,3158] [,3159] [,3160] [,3161] [,3162] [,3163] [,3164] [,3165] [,3166]
[1,] 0 0 0 1 0 0 1 1 1
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[6,] 0 1 0 0 0 0 1 1 1
[7,] 0 0 0 0 0 0 1 0 1
[8,] 1 0 0 0 0 0 1 1 0
[9,] 1 0 0 1 0 1 0 0 1
[10,] 1 1 0 1 0 0 0 0 0
[,3167] [,3168] [,3169] [,3170] [,3171] [,3172] [,3173] [,3174] [,3175]
[1,] 0 1 0 1 0 1 0 0 0
[2,] 1 0 0 1 0 0 1 0 0
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[9,] 0 1 0 0 0 1 0 0 0
[10,] 0 0 1 1 0 0 0 1 0
[,3176] [,3177] [,3178] [,3179] [,3180] [,3181] [,3182] [,3183] [,3184]
[1,] 0 1 1 0 0 0 0 0 0
[2,] 0 1 1 0 0 1 0 0 1
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[8,] 0 1 1 1 1 1 0 1 1
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[,3185] [,3186] [,3187] [,3188] [,3189] [,3190] [,3191] [,3192] [,3193]
[1,] 0 0 0 0 0 0 0 0 0
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[,3194] [,3195] [,3196] [,3197] [,3198] [,3199] [,3200] [,3201] [,3202]
[1,] 0 0 1 0 0 0 0 0 1
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[7,] 1 0 0 0 0 1 0 1 0
[8,] 0 0 1 1 0 1 0 1 1
[9,] 0 0 0 0 0 1 1 1 0
[10,] 1 0 1 0 0 0 0 0 0
[,3203] [,3204] [,3205] [,3206] [,3207] [,3208] [,3209] [,3210] [,3211]
[1,] 1 0 0 1 0 0 0 0 1
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[7,] 1 0 0 0 0 1 0 1 0
[8,] 0 0 0 1 0 0 0 1 0
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[,3212] [,3213] [,3214] [,3215] [,3216] [,3217] [,3218] [,3219] [,3220]
[1,] 0 0 0 1 0 1 1 1 1
[2,] 0 0 0 0 0 0 0 0 0
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[6,] 0 0 0 0 0 1 1 0 0
[7,] 0 0 0 0 0 0 0 1 0
[8,] 0 0 0 0 0 0 1 0 0
[9,] 0 0 0 0 1 0 1 0 0
[10,] 1 0 0 0 0 1 1 0 0
[,3221] [,3222] [,3223] [,3224] [,3225] [,3226] [,3227] [,3228] [,3229]
[1,] 0 0 0 1 0 0 0 0 0
[2,] 1 1 0 0 1 0 1 1 1
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[6,] 0 1 0 0 0 0 0 0 0
[7,] 0 0 0 0 0 0 0 0 1
[8,] 1 0 1 0 0 0 0 1 0
[9,] 0 0 0 0 1 1 1 1 0
[10,] 0 1 0 0 0 1 1 0 0
[,3230] [,3231] [,3232] [,3233] [,3234] [,3235] [,3236] [,3237] [,3238]
[1,] 0 0 0 0 1 0 1 0 0
[2,] 1 0 0 1 1 0 1 1 1
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[6,] 0 0 0 1 0 1 0 1 1
[7,] 1 0 0 1 1 1 0 0 1
[8,] 1 0 1 0 1 1 0 1 1
[9,] 0 0 0 0 0 0 0 0 0
[10,] 0 0 1 0 1 1 0 0 0
[,3239] [,3240] [,3241] [,3242] [,3243] [,3244] [,3245] [,3246] [,3247]
[1,] 0 0 0 0 0 0 0 0 0
[2,] 0 0 1 0 0 0 1 0 0
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[7,] 0 1 1 0 0 1 1 1 0
[8,] 0 1 0 0 0 0 0 0 0
[9,] 1 1 1 1 0 1 1 0 0
[10,] 0 1 0 0 0 0 1 0 0
[,3248] [,3249] [,3250] [,3251] [,3252] [,3253] [,3254] [,3255] [,3256]
[1,] 0 0 0 0 0 0 0 1 1
[2,] 1 0 0 0 1 0 0 0 1
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[6,] 1 0 1 0 1 1 0 1 1
[7,] 0 0 0 0 1 0 1 0 1
[8,] 0 0 0 1 0 0 0 0 1
[9,] 0 1 0 0 0 0 0 1 0
[10,] 0 0 0 1 0 0 1 0 1
[,3257] [,3258] [,3259] [,3260] [,3261] [,3262] [,3263] [,3264] [,3265]
[1,] 1 1 0 0 0 0 0 0 0
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[8,] 0 0 0 0 0 0 0 0 0
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[10,] 0 0 1 0 0 1 0 0 1
[,3266] [,3267] [,3268] [,3269] [,3270] [,3271] [,3272] [,3273] [,3274]
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[8,] 1 0 0 1 0 1 0 0 1
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[10,] 0 0 1 0 0 0 0 0 0
[,3275] [,3276] [,3277] [,3278] [,3279] [,3280] [,3281] [,3282] [,3283]
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[8,] 1 1 0 1 0 0 0 0 0
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[10,] 0 1 0 0 0 0 0 0 0
[,3284] [,3285] [,3286] [,3287] [,3288] [,3289] [,3290] [,3291] [,3292]
[1,] 0 0 0 0 0 0 1 0 0
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[10,] 0 0 0 0 0 0 1 0 0
[,3293] [,3294] [,3295] [,3296] [,3297] [,3298] [,3299] [,3300] [,3301]
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[10,] 0 0 1 1 0 0 0 0 0
[,3302] [,3303] [,3304] [,3305] [,3306] [,3307] [,3308] [,3309] [,3310]
[1,] 0 0 0 1 0 1 1 1 0
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[,3311] [,3312] [,3313] [,3314] [,3315] [,3316] [,3317] [,3318] [,3319]
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[10,] 0 0 0 0 0 0 0 0 0
[,3320] [,3321] [,3322] [,3323] [,3324] [,3325] [,3326] [,3327] [,3328]
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[10,] 0 0 0 0 0 0 0 1 1
[,3329] [,3330] [,3331] [,3332] [,3333] [,3334] [,3335] [,3336] [,3337]
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[,3338] [,3339] [,3340] [,3341] [,3342] [,3343] [,3344] [,3345] [,3346]
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[,3347] [,3348] [,3349] [,3350] [,3351] [,3352] [,3353] [,3354] [,3355]
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[,3356] [,3357] [,3358] [,3359] [,3360] [,3361] [,3362] [,3363] [,3364]
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[,3365] [,3366] [,3367] [,3368] [,3369] [,3370] [,3371] [,3372] [,3373]
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[,3374] [,3375] [,3376] [,3377] [,3378] [,3379] [,3380] [,3381] [,3382]
[1,] 1 0 0 0 1 0 1 0 1
[2,] 0 0 0 0 0 0 0 0 1
[3,] 0 1 0 0 0 0 0 1 0
[4,] 0 0 0 0 0 1 0 0 0
[5,] 1 0 0 0 0 0 1 0 1
[6,] 0 1 1 0 1 0 0 0 0
[7,] 0 0 0 0 0 0 0 0 0
[8,] 0 0 0 0 0 0 1 0 0
[9,] 0 0 1 1 0 1 1 0 0
[10,] 0 0 0 0 0 0 0 0 0
[,3383] [,3384] [,3385] [,3386] [,3387] [,3388] [,3389] [,3390] [,3391]
[1,] 0 0 1 0 0 0 0 1 0
[2,] 0 1 0 1 1 0 0 0 1
[3,] 0 0 0 0 1 0 0 1 1
[4,] 0 0 0 0 1 0 0 0 0
[5,] 0 0 0 0 0 1 0 0 0
[6,] 0 0 0 0 1 1 1 1 0
[7,] 0 1 1 0 0 1 0 0 0
[8,] 0 0 0 0 1 1 1 0 0
[9,] 0 1 0 0 0 0 0 0 0
[10,] 0 0 0 0 1 1 0 1 0
[,3392] [,3393] [,3394] [,3395] [,3396] [,3397] [,3398] [,3399] [,3400]
[1,] 1 1 0 0 1 0 0 1 0
[2,] 0 0 1 0 0 1 0 1 1
[3,] 0 0 0 0 0 0 0 0 1
[4,] 1 0 0 0 0 0 0 0 0
[5,] 0 0 1 0 0 1 0 0 0
[6,] 0 0 0 1 0 0 0 1 0
[7,] 1 0 1 1 0 0 0 0 0
[8,] 0 1 1 0 1 1 0 0 0
[9,] 0 0 0 0 1 0 0 0 0
[10,] 0 0 0 0 1 0 0 0 1
[,3401] [,3402] [,3403] [,3404] [,3405] [,3406] [,3407] [,3408] [,3409]
[1,] 0 0 0 0 1 1 0 0 0
[2,] 0 0 1 1 0 1 0 0 1
[3,] 0 0 0 1 0 0 0 1 0
[4,] 0 1 0 0 1 1 0 1 1
[5,] 0 0 0 0 0 1 0 1 0
[6,] 0 0 0 1 0 0 0 0 1
[7,] 0 1 0 0 0 0 0 1 0
[8,] 0 1 0 1 0 0 0 0 0
[9,] 0 0 0 0 0 0 0 0 0
[10,] 0 0 0 0 1 0 0 0 0
[,3410] [,3411] [,3412] [,3413] [,3414] [,3415] [,3416] [,3417] [,3418]
[1,] 0 1 0 0 1 0 0 0 0
[2,] 0 0 0 1 1 0 0 0 0
[3,] 0 0 0 0 1 1 1 1 0
[4,] 0 1 0 0 0 0 0 1 0
[5,] 0 0 0 1 0 0 0 0 0
[6,] 0 0 0 0 1 0 0 0 0
[7,] 0 0 1 0 0 1 0 0 0
[8,] 0 0 0 1 0 0 0 0 0
[9,] 0 0 0 1 0 0 1 1 0
[10,] 0 1 1 1 0 0 1 0 0
[,3419] [,3420] [,3421] [,3422] [,3423] [,3424] [,3425] [,3426] [,3427]
[1,] 0 0 0 1 0 0 0 0 0
[2,] 0 0 0 0 0 0 0 1 0
[3,] 0 0 0 0 1 1 1 1 1
[4,] 1 0 0 0 0 0 0 0 0
[5,] 0 0 1 0 0 1 1 1 0
[6,] 0 0 0 1 1 0 1 1 0
[7,] 1 1 0 0 0 1 1 1 0
[8,] 1 0 0 0 0 0 1 1 0
[9,] 1 0 0 0 1 0 0 1 0
[10,] 1 0 0 0 0 0 0 0 0
[,3428] [,3429] [,3430] [,3431] [,3432] [,3433] [,3434] [,3435] [,3436]
[1,] 0 1 1 1 0 0 1 1 1
[2,] 0 0 0 0 0 0 0 0 0
[3,] 1 0 0 0 0 0 0 0 0
[4,] 0 0 0 0 0 0 1 1 0
[5,] 0 1 0 0 0 0 0 0 0
[6,] 0 0 1 1 0 0 0 0 0
[7,] 0 0 0 0 1 0 0 0 0
[8,] 0 0 0 0 0 1 0 0 0
[9,] 1 0 0 0 0 0 0 0 0
[10,] 1 0 1 1 0 0 0 0 1
[,3437] [,3438] [,3439] [,3440] [,3441] [,3442] [,3443] [,3444] [,3445]
[1,] 0 0 0 0 0 0 0 0 0
[2,] 0 0 0 0 0 0 0 0 0
[3,] 0 0 0 1 0 0 0 1 0
[4,] 1 0 0 1 0 0 1 0 0
[5,] 0 0 1 0 1 1 0 1 0
[6,] 0 0 0 0 1 0 1 1 0
[7,] 0 1 0 0 0 1 0 0 0
[8,] 0 0 1 0 1 1 0 0 0
[9,] 0 0 0 1 0 0 0 0 0
[10,] 0 0 1 1 0 0 0 0 0
[,3446] [,3447] [,3448] [,3449] [,3450] [,3451] [,3452] [,3453] [,3454]
[1,] 0 0 0 0 0 0 0 0 1
[2,] 1 1 0 0 0 0 0 0 1
[3,] 0 1 1 0 0 0 0 1 0
[4,] 0 0 0 0 0 0 0 0 1
[5,] 0 0 1 0 0 0 1 0 1
[6,] 0 0 1 0 0 0 0 1 1
[7,] 0 0 0 0 0 0 0 1 1
[8,] 0 1 1 1 0 0 0 0 1
[9,] 1 0 0 1 0 0 1 0 0
[10,] 0 0 0 0 0 1 1 1 0
[,3455] [,3456] [,3457] [,3458] [,3459] [,3460] [,3461] [,3462] [,3463]
[1,] 0 1 1 1 0 0 0 0 0
[2,] 1 0 0 0 0 1 0 1 1
[3,] 0 0 1 1 0 0 0 0 0
[4,] 0 0 0 0 0 0 0 0 0
[5,] 0 1 0 0 0 0 1 0 1
[6,] 0 0 1 0 0 0 0 1 0
[7,] 1 0 0 1 0 0 0 0 1
[8,] 1 0 1 0 0 0 1 1 0
[9,] 0 0 0 0 0 0 0 0 0
[10,] 0 0 0 1 0 0 0 1 0
[,3464] [,3465] [,3466] [,3467] [,3468] [,3469] [,3470] [,3471] [,3472]
[1,] 1 0 0 0 0 0 1 0 0
[2,] 1 0 0 0 0 0 0 0 0
[3,] 0 0 0 1 0 0 0 1 1
[4,] 0 0 1 1 0 1 0 0 0
[5,] 1 1 0 0 0 0 0 0 0
[6,] 0 0 0 1 0 0 0 0 0
[7,] 0 0 0 0 1 0 0 1 0
[8,] 0 0 0 0 1 0 1 0 0
[9,] 0 0 0 0 0 1 0 0 0
[10,] 0 0 0 1 0 1 0 1 0
[,3473] [,3474] [,3475] [,3476] [,3477] [,3478] [,3479] [,3480] [,3481]
[1,] 0 0 0 1 1 0 0 0 0
[2,] 0 0 0 0 0 1 0 1 0
[3,] 1 0 0 0 0 0 0 1 0
[4,] 1 1 0 1 0 1 1 1 0
[5,] 1 1 0 0 0 0 0 0 1
[6,] 0 0 0 1 0 1 0 1 1
[7,] 0 0 0 0 0 1 0 0 0
[8,] 1 0 0 1 1 0 0 0 1
[9,] 0 0 0 0 0 0 0 0 0
[10,] 0 0 0 0 0 1 0 0 0
[,3482] [,3483] [,3484] [,3485] [,3486] [,3487] [,3488] [,3489] [,3490]
[1,] 0 0 0 0 0 1 0 1 1
[2,] 0 0 0 0 0 0 0 0 0
[3,] 0 0 1 1 0 0 0 0 1
[4,] 1 0 1 1 1 1 1 0 1
[5,] 1 0 0 0 0 0 0 0 1
[6,] 0 0 0 1 0 1 1 0 0
[7,] 0 0 0 0 0 0 1 1 0
[8,] 1 0 0 1 0 1 0 1 0
[9,] 1 0 0 0 1 0 0 0 0
[10,] 0 0 0 0 0 1 0 1 0
[,3491] [,3492] [,3493] [,3494] [,3495] [,3496] [,3497] [,3498] [,3499]
[1,] 0 0 0 0 0 0 1 0 0
[2,] 0 0 0 0 1 0 0 0 0
[3,] 0 0 0 0 1 1 1 0 0
[4,] 0 0 0 0 0 1 0 0 1
[5,] 0 0 1 0 0 1 0 0 0
[6,] 0 0 1 0 0 1 0 1 0
[7,] 0 1 1 0 0 1 0 0 0
[8,] 1 0 0 0 0 0 0 0 0
[9,] 1 0 0 1 1 0 0 0 0
[10,] 1 1 0 1 0 0 0 0 0
[,3500] [,3501] [,3502] [,3503] [,3504] [,3505] [,3506] [,3507] [,3508]
[1,] 0 0 0 0 0 0 0 0 1
[2,] 0 0 1 1 0 0 1 0 0
[3,] 0 0 0 0 0 0 0 0 1
[4,] 0 0 0 0 0 0 0 0 0
[5,] 0 0 0 0 0 0 1 0 0
[6,] 0 0 0 0 0 1 0 1 0
[7,] 0 1 0 0 0 0 1 0 1
[8,] 1 0 0 0 0 0 0 0 0
[9,] 0 0 0 1 1 0 0 0 0
[10,] 0 0 1 0 0 0 0 0 0
[,3509] [,3510] [,3511] [,3512] [,3513] [,3514] [,3515] [,3516] [,3517]
[1,] 1 0 1 1 0 0 1 1 1
[2,] 1 0 0 1 1 0 0 1 0
[3,] 0 0 0 0 0 1 1 0 0
[4,] 1 0 0 1 0 0 0 0 0
[5,] 1 0 0 1 0 1 0 0 0
[6,] 0 1 0 1 0 1 0 0 0
[7,] 0 0 1 1 0 0 0 0 1
[8,] 1 0 0 0 0 1 0 0 1
[9,] 0 0 0 0 0 0 1 0 0
[10,] 0 0 0 0 0 1 0 0 0
[,3518] [,3519] [,3520] [,3521] [,3522] [,3523] [,3524] [,3525] [,3526]
[1,] 0 0 0 0 0 1 0 1 0
[2,] 0 1 1 0 0 0 0 0 0
[3,] 0 0 0 1 1 0 0 1 0
[4,] 1 0 0 0 1 0 0 0 0
[5,] 0 0 1 0 0 0 0 1 1
[6,] 1 0 1 0 0 0 0 0 0
[7,] 1 1 0 0 0 0 1 0 0
[8,] 0 0 0 0 1 1 0 0 0
[9,] 1 0 0 0 0 0 0 1 0
[10,] 0 1 1 0 0 1 0 0 0
[,3527] [,3528] [,3529] [,3530] [,3531] [,3532] [,3533] [,3534] [,3535]
[1,] 0 0 0 0 0 0 0 0 0
[2,] 0 1 0 0 0 0 0 0 0
[3,] 0 1 1 1 0 1 1 0 0
[4,] 1 0 0 1 0 0 0 0 0
[5,] 0 0 0 1 1 0 0 0 0
[6,] 0 0 1 0 0 0 0 0 0
[7,] 0 0 0 1 1 0 1 0 0
[8,] 0 1 0 0 1 0 0 1 0
[9,] 1 0 1 0 0 0 0 0 0
[10,] 0 1 0 1 0 1 0 0 0
[,3536] [,3537] [,3538] [,3539] [,3540] [,3541] [,3542] [,3543] [,3544]
[1,] 0 1 1 0 0 1 0 0 1
[2,] 1 0 0 0 0 0 0 0 0
[3,] 0 0 0 0 0 0 0 1 0
[4,] 0 0 0 0 0 0 0 0 0
[5,] 0 1 1 0 1 0 0 1 1
[6,] 0 0 0 0 1 0 0 0 1
[7,] 1 0 1 1 0 0 0 0 0
[8,] 0 0 1 1 0 1 0 0 0
[9,] 0 1 0 0 0 1 0 0 0
[10,] 0 0 0 0 1 0 0 0 0
[,3545] [,3546] [,3547] [,3548] [,3549] [,3550] [,3551] [,3552] [,3553]
[1,] 0 0 0 1 0 1 1 0 0
[2,] 1 0 0 0 0 0 0 0 0
[3,] 0 1 0 1 0 0 1 0 0
[4,] 0 1 0 0 1 1 0 0 0
[5,] 0 0 1 0 1 1 0 0 0
[6,] 1 0 0 1 0 0 0 0 0
[7,] 1 0 1 0 0 1 0 1 0
[8,] 0 0 0 0 1 0 1 0 0
[9,] 0 1 0 0 0 1 0 0 0
[10,] 0 0 0 0 0 0 0 0 1
[,3554] [,3555] [,3556] [,3557] [,3558] [,3559] [,3560] [,3561] [,3562]
[1,] 0 1 0 0 0 0 1 0 1
[2,] 0 0 1 1 1 0 0 0 0
[3,] 0 0 0 0 1 1 1 1 0
[4,] 0 0 0 0 0 1 0 0 1
[5,] 0 0 0 0 1 1 0 1 1
[6,] 0 0 0 0 0 1 1 0 0
[7,] 0 0 0 1 0 0 1 1 1
[8,] 0 1 1 0 1 0 0 1 0
[9,] 0 0 0 0 1 0 0 0 1
[10,] 1 0 1 0 0 0 0 0 1
[,3563] [,3564] [,3565] [,3566] [,3567] [,3568] [,3569] [,3570] [,3571]
[1,] 0 0 0 1 0 1 1 0 0
[2,] 0 0 0 0 0 0 0 0 1
[3,] 1 0 0 0 0 0 0 0 0
[4,] 0 1 0 0 1 1 0 0 0
[5,] 1 0 1 1 0 1 0 0 0
[6,] 1 0 0 1 0 0 0 0 0
[7,] 0 1 1 0 0 0 0 0 0
[8,] 1 0 1 0 1 0 0 1 0
[9,] 1 0 0 0 1 0 1 0 1
[10,] 0 0 1 0 0 0 1 1 0
[,3572] [,3573] [,3574] [,3575] [,3576] [,3577] [,3578] [,3579] [,3580]
[1,] 0 1 0 1 1 0 0 0 0
[2,] 0 1 0 1 0 0 0 0 1
[3,] 0 0 0 0 1 0 0 0 1
[4,] 0 0 0 0 0 0 0 0 0
[5,] 0 1 1 0 1 0 0 0 0
[6,] 0 0 1 0 0 0 0 0 0
[7,] 0 0 0 1 0 1 0 0 1
[8,] 1 0 1 1 0 0 1 1 1
[9,] 1 0 0 0 0 0 0 0 0
[10,] 0 1 0 1 0 1 0 0 0
[,3581] [,3582] [,3583] [,3584] [,3585] [,3586] [,3587] [,3588] [,3589]
[1,] 0 1 0 0 0 0 1 0 0
[2,] 0 1 0 1 0 0 0 0 0
[3,] 1 0 0 0 0 0 0 0 0
[4,] 1 1 1 0 0 0 0 1 1
[5,] 0 0 0 0 0 0 0 1 0
[6,] 0 0 0 1 1 0 1 1 0
[7,] 1 0 0 1 0 0 0 0 0
[8,] 0 1 0 0 0 0 0 0 1
[9,] 0 1 0 0 1 1 1 0 0
[10,] 0 0 1 0 0 1 0 0 0
[,3590] [,3591] [,3592] [,3593] [,3594] [,3595] [,3596] [,3597] [,3598]
[1,] 0 0 0 0 0 0 0 0 1
[2,] 0 0 0 1 0 1 0 0 0
[3,] 0 0 0 0 1 0 1 1 0
[4,] 0 0 1 0 1 0 0 0 0
[5,] 0 0 0 0 1 1 0 0 1
[6,] 0 0 0 0 1 1 0 0 1
[7,] 1 0 1 0 0 0 0 1 1
[8,] 0 0 0 0 0 1 0 0 0
[9,] 0 0 0 0 0 1 0 0 0
[10,] 1 1 1 1 0 0 0 1 0
[,3599] [,3600] [,3601] [,3602] [,3603] [,3604] [,3605] [,3606] [,3607]
[1,] 0 0 0 0 0 1 0 0 0
[2,] 0 1 0 0 0 1 0 0 0
[3,] 0 0 0 0 0 0 0 0 0
[4,] 0 1 1 0 1 1 0 0 0
[5,] 0 0 0 1 0 1 0 1 0
[6,] 0 1 1 1 0 0 0 0 0
[7,] 1 0 1 1 0 0 0 0 1
[8,] 0 0 0 0 0 0 0 1 0
[9,] 0 1 1 0 0 0 1 0 1
[10,] 0 1 1 0 0 0 0 0 0
[,3608] [,3609] [,3610] [,3611] [,3612] [,3613] [,3614] [,3615] [,3616]
[1,] 0 1 0 0 1 0 0 0 0
[2,] 1 0 1 1 0 0 1 1 0
[3,] 1 0 0 1 1 1 0 1 0
[4,] 1 0 1 1 0 0 1 0 0
[5,] 1 0 0 0 0 0 0 0 1
[6,] 0 0 0 0 0 0 0 0 0
[7,] 0 1 1 0 0 0 1 0 0
[8,] 0 0 1 0 0 0 0 0 0
[9,] 0 0 1 0 0 1 0 0 0
[10,] 0 0 0 0 0 0 0 1 0
[,3617] [,3618] [,3619] [,3620] [,3621] [,3622] [,3623] [,3624] [,3625]
[1,] 0 0 1 0 1 0 0 0 1
[2,] 0 0 0 1 0 1 0 1 0
[3,] 0 0 0 1 1 0 1 0 0
[4,] 0 0 0 0 0 0 1 0 1
[5,] 1 0 0 1 0 1 0 1 0
[6,] 0 0 0 0 0 1 0 0 0
[7,] 0 0 0 0 0 0 0 1 0
[8,] 1 0 1 0 0 0 0 0 0
[9,] 0 0 1 0 0 1 0 0 0
[10,] 0 0 0 0 0 0 1 0 0
[,3626] [,3627] [,3628] [,3629] [,3630] [,3631] [,3632] [,3633] [,3634]
[1,] 0 0 0 0 0 0 0 0 0
[2,] 1 0 0 1 0 0 0 0 0
[3,] 0 1 0 1 0 0 0 0 0
[4,] 0 0 0 0 1 0 0 0 0
[5,] 0 0 1 0 0 0 0 0 0
[6,] 1 0 0 1 0 0 0 0 0
[7,] 0 1 0 0 0 0 0 0 0
[8,] 0 1 0 0 0 0 1 0 0
[9,] 0 0 0 0 1 0 0 0 0
[10,] 1 0 0 0 0 0 0 0 0
[,3635] [,3636] [,3637] [,3638] [,3639] [,3640] [,3641] [,3642] [,3643]
[1,] 0 1 0 0 0 0 1 0 1
[2,] 0 0 1 0 0 0 1 0 0
[3,] 0 0 0 1 0 0 0 1 1
[4,] 0 0 0 1 1 0 0 0 0
[5,] 0 1 0 0 1 1 0 0 1
[6,] 0 1 1 0 0 0 0 0 1
[7,] 0 0 0 0 0 0 1 0 0
[8,] 1 1 0 1 0 0 0 0 0
[9,] 0 0 0 0 1 0 1 1 0
[10,] 0 0 1 0 0 1 0 0 1
[,3644] [,3645] [,3646] [,3647] [,3648] [,3649] [,3650] [,3651] [,3652]
[1,] 0 0 0 0 0 0 1 0 0
[2,] 0 1 0 0 0 0 0 0 0
[3,] 0 0 0 0 0 1 0 0 0
[4,] 0 0 1 1 0 0 0 0 0
[5,] 0 0 0 0 0 1 0 0 0
[6,] 0 0 1 0 0 0 1 0 0
[7,] 1 1 0 0 1 0 1 0 0
[8,] 0 0 1 1 1 1 0 0 0
[9,] 1 1 0 0 1 0 0 0 0
[10,] 0 0 1 0 0 0 0 0 0
[,3653] [,3654] [,3655] [,3656] [,3657] [,3658] [,3659] [,3660] [,3661]
[1,] 0 1 0 0 0 0 0 1 0
[2,] 1 0 0 1 0 1 0 0 0
[3,] 0 0 0 1 0 0 0 0 1
[4,] 1 0 0 1 0 0 1 1 0
[5,] 0 0 0 1 0 0 0 0 0
[6,] 0 0 1 0 0 0 0 0 0
[7,] 0 0 0 1 1 0 1 1 1
[8,] 0 1 0 0 1 0 0 0 0
[9,] 1 0 0 1 0 1 0 0 1
[10,] 0 0 0 1 1 0 0 0 0
[,3662] [,3663] [,3664] [,3665] [,3666] [,3667] [,3668] [,3669] [,3670]
[1,] 1 0 0 0 0 1 0 0 0
[2,] 0 0 1 0 1 0 1 1 1
[3,] 0 1 1 0 1 0 1 1 0
[4,] 0 0 0 0 0 0 0 1 0
[5,] 0 0 0 0 0 0 1 0 0
[6,] 0 0 0 1 0 1 0 1 1
[7,] 0 0 0 1 0 0 0 0 1
[8,] 0 0 0 0 1 0 0 0 0
[9,] 0 0 0 1 1 0 1 0 0
[10,] 0 0 0 1 0 1 0 0 0
[,3671] [,3672] [,3673] [,3674] [,3675] [,3676] [,3677] [,3678] [,3679]
[1,] 0 1 0 1 0 0 0 0 0
[2,] 0 0 0 1 0 0 0 1 0
[3,] 1 0 0 1 0 0 0 0 1
[4,] 0 0 0 0 0 0 1 0 1
[5,] 1 0 0 0 0 0 0 0 1
[6,] 0 1 0 0 0 0 1 1 0
[7,] 1 0 0 0 0 0 1 0 0
[8,] 0 0 0 0 0 0 1 0 0
[9,] 1 0 0 0 1 0 0 1 0
[10,] 0 0 0 0 0 0 0 0 0
[,3680] [,3681] [,3682] [,3683] [,3684] [,3685] [,3686] [,3687] [,3688]
[1,] 1 1 0 0 0 0 0 0 0
[2,] 0 1 0 0 0 0 0 1 0
[3,] 1 0 0 0 0 1 1 0 0
[4,] 0 0 1 1 1 0 0 0 0
[5,] 1 0 1 0 1 0 0 0 0
[6,] 0 0 0 0 0 0 0 1 1
[7,] 1 0 0 0 1 0 0 0 0
[8,] 0 0 1 0 0 0 0 0 1
[9,] 1 0 1 0 0 1 0 1 1
[10,] 0 1 1 0 1 1 0 0 0
[,3689] [,3690] [,3691] [,3692] [,3693] [,3694] [,3695] [,3696] [,3697]
[1,] 1 0 0 1 0 0 0 1 1
[2,] 1 0 0 0 0 0 1 0 0
[3,] 0 1 0 0 0 0 0 0 0
[4,] 1 0 0 0 0 0 0 0 0
[5,] 0 0 0 0 0 0 0 0 1
[6,] 0 0 0 0 0 0 0 0 1
[7,] 0 0 0 1 0 0 0 0 0
[8,] 0 1 1 0 0 1 0 1 1
[9,] 1 0 0 0 0 0 1 1 0
[10,] 0 0 1 0 0 1 0 1 0
[,3698] [,3699] [,3700] [,3701] [,3702] [,3703] [,3704] [,3705] [,3706]
[1,] 1 0 0 1 1 0 0 1 0
[2,] 0 0 0 0 0 0 0 0 0
[3,] 1 0 0 1 1 1 0 0 1
[4,] 0 0 0 0 0 1 0 0 1
[5,] 0 0 0 0 0 0 0 0 0
[6,] 0 0 0 0 0 1 1 0 0
[7,] 0 0 0 0 0 0 0 1 1
[8,] 1 0 0 1 0 0 0 0 0
[9,] 0 0 0 0 0 0 0 0 0
[10,] 0 0 0 0 1 1 0 0 0
[,3707] [,3708] [,3709] [,3710] [,3711] [,3712] [,3713] [,3714] [,3715]
[1,] 0 0 1 0 0 1 0 0 1
[2,] 1 0 0 0 0 0 1 0 0
[3,] 0 1 0 1 0 0 0 0 0
[4,] 0 0 0 0 0 1 0 0 0
[5,] 1 1 1 0 1 0 0 1 1
[6,] 0 0 0 0 1 1 0 0 0
[7,] 0 0 0 0 0 1 0 0 0
[8,] 0 0 0 1 1 1 1 0 0
[9,] 0 0 0 0 1 0 0 0 0
[10,] 1 0 0 0 0 1 0 0 1
[,3716] [,3717] [,3718] [,3719] [,3720] [,3721] [,3722] [,3723] [,3724]
[1,] 0 0 0 1 0 0 0 1 1
[2,] 0 1 0 0 0 0 0 1 0
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[6,] 0 1 0 0 0 1 0 0 1
[7,] 0 0 0 1 0 1 1 0 1
[8,] 0 0 0 0 0 1 0 0 1
[9,] 0 1 0 0 1 0 1 1 1
[10,] 0 1 0 0 0 0 0 0 1
[,3725] [,3726] [,3727] [,3728] [,3729] [,3730] [,3731] [,3732] [,3733]
[1,] 0 0 0 0 0 0 0 0 1
[2,] 0 1 0 1 0 0 0 0 0
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[4,] 1 0 1 0 0 0 0 0 0
[5,] 0 0 0 0 1 0 0 0 0
[6,] 1 1 1 1 1 0 1 0 0
[7,] 0 0 0 0 0 0 0 0 0
[8,] 0 0 0 1 0 1 0 1 1
[9,] 0 0 0 0 0 1 0 0 0
[10,] 0 0 0 0 1 1 0 1 1
[,3734] [,3735] [,3736] [,3737] [,3738] [,3739] [,3740] [,3741] [,3742]
[1,] 1 0 0 0 0 0 0 1 0
[2,] 0 1 0 1 0 1 0 0 1
[3,] 0 0 0 0 1 1 0 1 0
[4,] 0 0 0 0 0 0 0 1 0
[5,] 0 0 1 0 0 1 0 0 0
[6,] 0 0 0 0 0 0 0 1 1
[7,] 0 1 1 1 1 0 0 1 0
[8,] 0 0 0 0 0 0 0 0 0
[9,] 0 0 0 1 1 0 0 0 1
[10,] 1 1 0 1 0 0 0 0 1
[,3743] [,3744] [,3745] [,3746] [,3747] [,3748] [,3749] [,3750] [,3751]
[1,] 0 0 0 0 0 0 1 1 0
[2,] 0 0 0 0 0 0 1 0 0
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[5,] 1 0 0 0 1 0 0 0 0
[6,] 0 0 0 0 0 1 0 1 1
[7,] 0 1 0 0 0 1 0 0 0
[8,] 0 0 0 0 0 0 1 1 0
[9,] 1 0 1 0 0 1 0 0 1
[10,] 0 0 0 1 0 1 0 0 0
[,3752] [,3753] [,3754] [,3755] [,3756] [,3757] [,3758] [,3759] [,3760]
[1,] 0 0 0 1 0 0 0 0 0
[2,] 0 0 0 0 1 0 0 0 1
[3,] 0 1 0 0 1 0 1 0 0
[4,] 0 0 1 0 0 1 0 0 0
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[6,] 0 0 1 0 0 0 0 0 0
[7,] 0 1 0 0 0 0 0 0 0
[8,] 1 0 0 1 1 0 0 0 0
[9,] 1 0 0 1 1 0 0 0 0
[10,] 0 0 0 1 1 0 0 0 1
[,3761] [,3762] [,3763] [,3764] [,3765] [,3766] [,3767] [,3768] [,3769]
[1,] 0 0 0 0 0 1 0 0 0
[2,] 0 0 1 1 0 0 1 0 0
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[6,] 0 0 0 1 0 1 1 0 0
[7,] 0 1 0 0 0 0 0 0 0
[8,] 0 0 0 0 0 0 0 0 1
[9,] 0 1 0 0 0 0 0 1 0
[10,] 0 0 0 1 0 0 0 0 0
[,3770] [,3771] [,3772] [,3773] [,3774] [,3775] [,3776] [,3777] [,3778]
[1,] 0 0 0 0 1 0 0 0 1
[2,] 1 0 0 0 1 0 1 0 0
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[4,] 1 0 0 0 0 0 1 0 0
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[6,] 0 1 0 1 0 1 1 0 0
[7,] 0 1 0 0 0 0 0 0 0
[8,] 0 0 1 0 0 0 0 0 0
[9,] 0 1 0 1 0 1 0 0 1
[10,] 0 1 0 0 1 1 0 0 0
[,3779] [,3780] [,3781] [,3782] [,3783] [,3784] [,3785] [,3786] [,3787]
[1,] 1 0 1 0 0 0 0 0 0
[2,] 1 1 0 0 1 0 0 1 1
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[4,] 1 1 0 1 0 0 0 0 1
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[6,] 0 1 1 1 1 0 0 0 0
[7,] 0 0 1 1 0 1 0 1 0
[8,] 0 0 0 0 0 0 1 0 0
[9,] 1 1 1 0 0 0 1 1 1
[10,] 0 0 0 0 0 0 0 0 1
[,3788] [,3789] [,3790] [,3791] [,3792] [,3793] [,3794] [,3795] [,3796]
[1,] 0 0 0 0 0 0 0 1 1
[2,] 0 0 0 0 0 1 0 0 0
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[4,] 0 0 0 0 1 1 1 1 0
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[6,] 0 0 0 0 1 0 0 0 1
[7,] 0 1 0 0 0 1 1 0 0
[8,] 0 0 0 0 1 0 0 1 0
[9,] 1 0 0 1 0 1 0 1 1
[10,] 0 0 0 0 0 0 1 0 0
[,3797] [,3798] [,3799] [,3800] [,3801] [,3802] [,3803] [,3804] [,3805]
[1,] 0 1 1 1 0 1 0 0 1
[2,] 1 1 0 1 0 0 0 0 1
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[6,] 0 0 0 0 1 0 0 1 0
[7,] 0 0 1 0 1 0 0 0 0
[8,] 1 0 0 0 0 0 0 1 0
[9,] 0 0 0 0 0 0 1 1 1
[10,] 0 0 0 0 0 0 0 0 1
[,3806] [,3807] [,3808] [,3809] [,3810] [,3811] [,3812] [,3813] [,3814]
[1,] 1 0 0 0 0 0 1 0 0
[2,] 0 0 0 0 0 1 0 0 1
[3,] 0 0 0 1 0 0 1 0 1
[4,] 1 0 0 0 0 0 1 0 0
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[6,] 0 0 0 1 0 0 0 0 1
[7,] 0 0 0 0 1 1 0 0 0
[8,] 0 1 1 0 1 1 0 0 1
[9,] 0 0 0 0 1 1 1 0 0
[10,] 0 0 0 0 0 1 1 1 1
[,3815] [,3816] [,3817] [,3818] [,3819] [,3820] [,3821] [,3822] [,3823]
[1,] 1 1 0 0 0 1 1 0 0
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[6,] 1 0 0 0 1 0 0 1 0
[7,] 1 1 0 1 0 0 0 1 1
[8,] 0 0 0 0 1 1 0 0 0
[9,] 0 0 0 0 0 0 0 0 1
[10,] 0 1 0 0 1 1 1 0 1
[,3824] [,3825] [,3826] [,3827] [,3828] [,3829] [,3830] [,3831] [,3832]
[1,] 0 0 0 0 0 1 1 0 0
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[6,] 1 0 0 0 1 0 1 0 1
[7,] 0 1 0 0 0 0 0 1 0
[8,] 0 0 0 0 0 0 1 0 0
[9,] 0 0 1 0 0 0 0 0 0
[10,] 0 0 1 1 1 0 0 1 0
[,3833] [,3834] [,3835] [,3836] [,3837] [,3838] [,3839] [,3840] [,3841]
[1,] 0 1 0 0 1 0 1 0 0
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[4,] 1 0 0 1 0 0 1 1 0
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[7,] 1 1 0 0 0 1 0 0 0
[8,] 0 0 0 0 0 0 0 0 0
[9,] 0 1 0 0 0 0 0 1 0
[10,] 0 0 0 0 0 0 0 0 0
[,3842] [,3843] [,3844] [,3845] [,3846] [,3847] [,3848] [,3849] [,3850]
[1,] 1 0 0 1 0 1 0 0 0
[2,] 0 1 1 1 1 0 1 1 0
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[4,] 1 1 0 0 0 0 1 1 0
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[6,] 1 0 1 1 0 1 0 1 0
[7,] 1 0 0 0 0 0 1 0 0
[8,] 1 0 0 0 0 1 0 0 0
[9,] 0 0 0 0 1 0 0 0 0
[10,] 0 0 0 0 0 1 1 1 0
[,3851] [,3852] [,3853] [,3854] [,3855] [,3856] [,3857] [,3858] [,3859]
[1,] 0 0 0 1 0 0 0 1 0
[2,] 0 0 0 0 0 0 1 1 0
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[7,] 0 0 1 0 0 0 0 0 1
[8,] 1 0 1 1 0 0 0 0 1
[9,] 0 0 1 1 0 0 1 0 0
[10,] 0 1 0 0 0 1 0 1 1
[,3860] [,3861] [,3862] [,3863] [,3864] [,3865] [,3866] [,3867] [,3868]
[1,] 1 0 1 1 0 1 0 0 0
[2,] 0 0 0 0 0 0 0 1 0
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[4,] 0 0 0 0 0 0 0 1 1
[5,] 0 0 1 0 0 0 0 0 1
[6,] 0 0 1 0 0 1 0 0 0
[7,] 1 0 1 0 0 0 0 0 0
[8,] 0 0 0 0 0 0 1 0 1
[9,] 0 0 0 0 1 0 0 0 0
[10,] 0 0 0 0 1 0 1 1 1
[,3869] [,3870] [,3871] [,3872] [,3873] [,3874] [,3875] [,3876] [,3877]
[1,] 1 0 0 0 0 0 1 0 1
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[7,] 0 0 1 0 0 0 0 0 0
[8,] 0 0 1 0 0 1 0 0 0
[9,] 0 1 1 0 1 1 1 0 0
[10,] 0 0 1 0 0 0 0 0 1
[,3878] [,3879] [,3880] [,3881] [,3882] [,3883] [,3884] [,3885] [,3886]
[1,] 0 0 0 1 1 0 0 0 0
[2,] 0 1 0 1 0 0 1 0 0
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[6,] 1 1 0 0 1 0 0 0 0
[7,] 0 0 0 0 0 1 0 0 0
[8,] 1 0 0 1 0 0 1 0 0
[9,] 1 0 0 1 0 0 0 0 0
[10,] 0 0 0 0 0 0 0 1 0
[,3887] [,3888] [,3889] [,3890] [,3891] [,3892] [,3893] [,3894] [,3895]
[1,] 0 0 1 0 0 0 1 0 0
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[7,] 0 0 0 0 1 0 0 0 0
[8,] 0 0 0 0 1 1 1 1 0
[9,] 0 0 0 0 0 0 0 0 1
[10,] 0 0 1 1 0 0 1 0 1
[,3896] [,3897] [,3898] [,3899] [,3900] [,3901] [,3902] [,3903] [,3904]
[1,] 1 0 0 0 1 1 1 0 0
[2,] 1 1 0 0 1 0 0 0 1
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[5,] 0 0 0 0 1 0 0 1 0
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[7,] 0 0 0 0 0 0 0 0 0
[8,] 0 1 1 0 0 0 0 0 1
[9,] 0 1 0 1 0 0 0 1 0
[10,] 0 0 1 1 0 0 0 1 0
[,3905] [,3906] [,3907] [,3908] [,3909] [,3910] [,3911] [,3912] [,3913]
[1,] 0 0 0 0 0 0 0 0 0
[2,] 1 0 1 0 0 0 1 1 0
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[7,] 0 0 0 0 0 1 0 1 0
[8,] 0 0 0 0 1 0 1 0 0
[9,] 0 1 1 0 0 0 1 0 1
[10,] 1 0 1 0 0 0 1 0 1
[,3914] [,3915] [,3916] [,3917] [,3918] [,3919] [,3920] [,3921] [,3922]
[1,] 0 0 0 1 0 0 0 0 0
[2,] 1 1 0 0 0 1 0 0 0
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[6,] 1 0 1 0 1 0 1 0 1
[7,] 1 0 0 0 1 1 0 0 0
[8,] 0 0 0 1 0 0 0 0 1
[9,] 0 0 1 0 0 0 0 1 0
[10,] 0 0 0 0 0 1 0 0 0
[,3923] [,3924] [,3925] [,3926] [,3927] [,3928] [,3929] [,3930] [,3931]
[1,] 1 0 0 0 0 0 0 0 0
[2,] 0 0 0 0 0 0 1 0 1
[3,] 0 0 0 1 0 0 1 1 1
[4,] 0 0 0 0 0 1 0 1 1
[5,] 1 1 0 1 0 0 0 0 0
[6,] 0 1 1 1 0 0 0 0 0
[7,] 1 1 0 0 1 0 0 0 1
[8,] 0 0 0 0 0 1 0 0 0
[9,] 0 1 0 1 0 1 1 1 0
[10,] 0 1 0 0 0 0 0 0 1
[,3932] [,3933] [,3934] [,3935] [,3936] [,3937] [,3938] [,3939] [,3940]
[1,] 1 0 0 1 0 0 0 0 0
[2,] 0 0 0 1 0 1 1 1 0
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[5,] 0 1 0 0 0 0 0 0 0
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[7,] 1 0 0 0 0 1 0 1 0
[8,] 0 1 0 0 0 0 1 0 0
[9,] 0 0 0 0 1 0 0 0 1
[10,] 0 0 1 0 0 1 0 0 0
[,3941] [,3942] [,3943] [,3944] [,3945] [,3946] [,3947] [,3948] [,3949]
[1,] 1 0 0 0 0 0 1 1 0
[2,] 0 0 0 0 1 0 0 0 0
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[5,] 1 1 0 0 1 0 0 1 1
[6,] 1 1 0 0 0 0 0 1 0
[7,] 1 0 0 0 0 1 0 1 0
[8,] 1 0 0 0 0 0 0 0 1
[9,] 0 0 0 0 1 0 0 0 0
[10,] 1 0 1 0 0 1 0 0 0
[,3950] [,3951] [,3952] [,3953] [,3954] [,3955] [,3956] [,3957] [,3958]
[1,] 1 0 1 0 0 0 0 0 0
[2,] 0 0 1 0 0 0 0 0 0
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[5,] 0 1 0 1 0 1 0 1 0
[6,] 1 0 0 0 0 0 1 0 1
[7,] 0 0 0 0 0 1 1 0 0
[8,] 0 0 1 1 0 0 1 0 1
[9,] 0 0 1 0 0 0 0 1 0
[10,] 0 0 0 0 0 0 0 1 0
[,3959] [,3960] [,3961] [,3962] [,3963] [,3964] [,3965] [,3966] [,3967]
[1,] 1 0 0 0 0 0 0 1 0
[2,] 1 0 0 0 1 0 1 0 0
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[5,] 1 1 0 1 1 0 0 1 0
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[7,] 0 0 1 0 0 0 0 0 0
[8,] 0 0 0 0 0 0 0 0 0
[9,] 0 0 1 1 0 0 0 1 0
[10,] 0 1 0 0 0 0 0 1 0
[,3968] [,3969] [,3970] [,3971] [,3972] [,3973] [,3974] [,3975] [,3976]
[1,] 1 0 1 0 0 0 0 1 0
[2,] 0 0 0 0 0 0 0 0 1
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[7,] 1 0 0 0 0 0 0 0 0
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[9,] 0 0 1 1 0 0 0 0 1
[10,] 0 0 0 1 0 0 1 0 0
[,3977] [,3978] [,3979] [,3980] [,3981] [,3982] [,3983] [,3984] [,3985]
[1,] 0 1 0 1 0 1 0 1 0
[2,] 1 0 0 0 0 0 0 0 1
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[8,] 0 0 0 0 0 0 0 1 0
[9,] 0 1 0 1 0 0 0 0 0
[10,] 0 0 0 1 0 0 0 1 0
[,3986] [,3987] [,3988] [,3989] [,3990] [,3991] [,3992] [,3993] [,3994]
[1,] 1 0 0 1 0 0 0 0 0
[2,] 1 1 0 0 0 0 0 1 1
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[7,] 0 1 0 0 1 1 0 1 1
[8,] 0 0 0 1 1 1 0 0 0
[9,] 1 0 0 1 0 1 1 1 0
[10,] 1 0 0 1 0 1 0 1 0
[,3995] [,3996] [,3997] [,3998] [,3999] [,4000] [,4001] [,4002] [,4003]
[1,] 0 0 1 0 0 0 0 0 0
[2,] 0 0 1 0 0 0 0 0 1
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[7,] 0 0 1 0 1 1 1 0 1
[8,] 0 0 0 0 0 0 0 0 0
[9,] 0 0 0 1 0 0 0 1 1
[10,] 0 0 0 1 0 0 0 1 0
[,4004] [,4005] [,4006] [,4007] [,4008] [,4009] [,4010] [,4011] [,4012]
[1,] 1 0 1 0 0 0 0 0 1
[2,] 0 0 0 0 0 0 0 0 0
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[7,] 0 0 0 0 0 1 0 0 0
[8,] 1 1 0 1 0 0 0 0 0
[9,] 0 0 0 1 1 1 0 0 1
[10,] 0 1 0 0 1 0 1 1 0
[,4013] [,4014] [,4015] [,4016] [,4017] [,4018] [,4019] [,4020] [,4021]
[1,] 0 0 0 0 0 0 1 0 0
[2,] 0 0 0 0 0 0 0 1 0
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[7,] 0 0 0 1 1 0 1 1 0
[8,] 1 0 0 0 0 0 1 0 0
[9,] 1 0 1 0 1 0 0 1 0
[10,] 1 1 0 0 0 0 0 0 1
[,4022] [,4023] [,4024] [,4025] [,4026] [,4027] [,4028] [,4029] [,4030]
[1,] 0 1 1 0 0 0 0 0 1
[2,] 0 0 0 0 1 0 0 0 0
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[7,] 0 1 0 0 0 0 1 0 0
[8,] 0 1 1 0 0 0 1 1 0
[9,] 0 0 1 0 0 1 1 0 0
[10,] 0 1 1 1 0 0 1 0 0
[,4031] [,4032] [,4033] [,4034] [,4035] [,4036] [,4037] [,4038] [,4039]
[1,] 0 1 0 1 1 1 0 0 1
[2,] 0 0 0 0 0 0 1 0 0
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[6,] 0 1 0 0 0 1 1 0 1
[7,] 0 1 0 1 0 0 1 1 0
[8,] 0 1 0 0 0 0 0 0 0
[9,] 0 1 0 1 0 0 0 1 0
[10,] 0 0 1 1 0 1 0 0 0
[,4040] [,4041] [,4042] [,4043] [,4044] [,4045] [,4046] [,4047] [,4048]
[1,] 0 0 1 0 0 0 1 1 1
[2,] 0 0 1 1 0 1 1 0 0
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[8,] 0 1 0 1 0 0 0 0 1
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[10,] 0 0 0 0 0 0 1 0 1
[,4049] [,4050] [,4051] [,4052] [,4053] [,4054] [,4055] [,4056] [,4057]
[1,] 1 0 1 0 0 1 0 0 0
[2,] 0 1 0 0 0 0 0 1 1
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[,4058] [,4059] [,4060] [,4061] [,4062] [,4063] [,4064] [,4065] [,4066]
[1,] 0 0 0 1 0 1 0 1 0
[2,] 1 0 0 0 1 0 1 1 1
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[8,] 0 1 0 0 0 0 0 1 1
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[10,] 0 0 0 0 1 1 0 0 0
[,4067] [,4068] [,4069] [,4070] [,4071] [,4072] [,4073] [,4074] [,4075]
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[,4076] [,4077] [,4078] [,4079] [,4080] [,4081] [,4082] [,4083] [,4084]
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[8,] 1 1 0 0 1 0 1 0 1
[9,] 0 1 0 1 0 0 0 1 1
[10,] 1 0 0 0 0 0 1 0 1
[,4085] [,4086] [,4087] [,4088] [,4089] [,4090] [,4091] [,4092] [,4093]
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[,4094] [,4095] [,4096] [,4097] [,4098] [,4099] [,4100] [,4101] [,4102]
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[10,] 0 1 1 0 0 0 0 1 0
[,4103] [,4104] [,4105] [,4106] [,4107] [,4108] [,4109] [,4110] [,4111]
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[,4112] [,4113] [,4114] [,4115] [,4116] [,4117] [,4118] [,4119] [,4120]
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[,4121] [,4122] [,4123] [,4124] [,4125] [,4126] [,4127] [,4128] [,4129]
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[4,] 0 1 0 0 0 0 0 0 0
[5,] 0 0 0 0 0 0 1 0 1
[6,] 0 0 1 0 1 0 0 0 0
[7,] 0 0 1 0 0 0 1 1 0
[8,] 1 1 0 0 0 0 1 0 0
[9,] 0 1 0 0 0 1 0 1 0
[10,] 0 0 1 0 0 0 0 0 0
[,4130] [,4131] [,4132] [,4133] [,4134] [,4135] [,4136] [,4137] [,4138]
[1,] 0 0 0 0 0 0 1 0 1
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[4,] 1 0 1 0 0 0 0 1 0
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[6,] 1 1 0 0 0 0 0 0 0
[7,] 0 0 0 0 0 0 1 1 1
[8,] 0 1 0 0 1 0 0 0 0
[9,] 0 1 0 0 0 0 1 1 0
[10,] 0 1 0 0 0 0 1 1 0
[,4139] [,4140] [,4141] [,4142] [,4143] [,4144] [,4145] [,4146] [,4147]
[1,] 0 0 1 0 0 1 0 0 0
[2,] 0 0 0 0 0 1 1 1 0
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[6,] 0 0 1 0 0 0 1 1 1
[7,] 0 0 0 0 1 0 0 0 1
[8,] 0 0 1 0 0 1 0 1 0
[9,] 1 0 0 1 0 0 0 0 0
[10,] 0 0 0 1 0 1 1 0 0
[,4148] [,4149] [,4150] [,4151] [,4152] [,4153] [,4154] [,4155] [,4156]
[1,] 0 1 0 0 1 1 0 0 0
[2,] 0 1 0 0 0 0 1 0 1
[3,] 0 0 0 0 0 0 1 0 1
[4,] 0 0 0 1 1 0 0 0 0
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[6,] 0 1 0 0 0 0 1 0 0
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[8,] 1 1 0 1 0 1 0 0 1
[9,] 0 0 1 0 0 0 0 0 1
[10,] 0 0 0 1 0 1 0 1 0
[,4157] [,4158] [,4159] [,4160] [,4161] [,4162] [,4163] [,4164] [,4165]
[1,] 0 0 0 0 0 0 0 0 0
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[8,] 0 0 0 0 1 0 0 0 0
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[,4166] [,4167] [,4168] [,4169] [,4170] [,4171] [,4172] [,4173] [,4174]
[1,] 1 0 1 0 0 0 1 0 0
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[7,] 0 1 0 0 0 1 0 1 1
[8,] 1 0 1 0 1 0 0 1 0
[9,] 0 1 0 0 0 0 0 0 0
[10,] 1 0 1 0 1 1 0 0 0
[,4175] [,4176] [,4177] [,4178] [,4179] [,4180] [,4181] [,4182] [,4183]
[1,] 0 0 1 0 0 0 0 0 0
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[8,] 0 0 0 0 0 1 0 0 0
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[,4184] [,4185] [,4186] [,4187] [,4188] [,4189] [,4190] [,4191] [,4192]
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[8,] 0 1 0 1 1 0 1 0 1
[9,] 0 1 0 0 1 0 1 0 0
[10,] 1 0 0 0 0 0 0 0 0
[,4193] [,4194] [,4195] [,4196] [,4197] [,4198] [,4199] [,4200] [,4201]
[1,] 0 1 0 1 0 1 0 0 0
[2,] 1 1 0 0 0 1 0 1 0
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[7,] 0 0 1 0 0 0 1 1 0
[8,] 0 0 0 0 0 1 0 1 0
[9,] 0 1 1 0 0 0 0 0 0
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[,4202] [,4203] [,4204] [,4205] [,4206] [,4207] [,4208] [,4209] [,4210]
[1,] 1 0 1 0 1 0 1 0 1
[2,] 0 0 0 0 0 0 1 0 1
[3,] 0 1 0 1 0 1 0 0 0
[4,] 0 1 1 0 0 1 0 1 0
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[6,] 0 0 0 0 0 0 1 0 1
[7,] 0 0 1 0 0 0 1 0 0
[8,] 0 0 0 1 1 1 1 0 0
[9,] 0 0 0 0 0 0 0 0 1
[10,] 0 0 0 0 0 0 0 1 0
[,4211] [,4212] [,4213] [,4214] [,4215] [,4216] [,4217] [,4218] [,4219]
[1,] 0 0 0 0 0 1 0 0 0
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[7,] 1 0 1 0 0 0 0 0 1
[8,] 0 1 0 1 0 1 0 0 0
[9,] 1 0 0 0 0 0 0 0 0
[10,] 1 1 1 0 0 0 0 0 0
[,4220] [,4221] [,4222] [,4223] [,4224] [,4225] [,4226] [,4227] [,4228]
[1,] 0 0 0 0 0 1 0 1 0
[2,] 0 0 0 1 1 0 0 0 0
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[4,] 0 0 0 1 0 0 0 1 1
[5,] 0 0 0 0 0 0 0 0 1
[6,] 0 1 0 0 1 0 0 0 0
[7,] 0 1 0 0 1 1 0 1 0
[8,] 0 0 0 0 0 0 0 0 0
[9,] 0 0 1 1 0 0 0 1 1
[10,] 1 0 0 0 0 1 1 1 1
[,4229] [,4230] [,4231] [,4232] [,4233] [,4234] [,4235] [,4236] [,4237]
[1,] 0 0 0 0 0 0 0 1 0
[2,] 0 1 1 0 0 1 0 0 1
[3,] 0 1 0 0 1 0 0 1 0
[4,] 1 1 1 0 0 0 1 1 1
[5,] 0 0 0 1 1 0 0 0 1
[6,] 0 1 0 0 0 0 1 1 0
[7,] 0 0 1 0 0 0 0 1 0
[8,] 0 0 0 0 1 0 0 0 0
[9,] 0 1 0 0 0 1 1 0 1
[10,] 0 0 1 1 1 0 1 1 0
[,4238] [,4239] [,4240] [,4241] [,4242] [,4243] [,4244] [,4245] [,4246]
[1,] 1 0 0 0 0 1 0 1 0
[2,] 0 0 1 0 0 0 0 0 0
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[4,] 0 0 0 0 0 0 1 0 0
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[7,] 0 0 1 0 1 1 0 0 0
[8,] 0 0 0 0 0 0 0 0 0
[9,] 0 0 1 1 0 0 0 0 0
[10,] 0 0 0 0 0 0 0 1 1
[,4247] [,4248] [,4249] [,4250] [,4251] [,4252] [,4253] [,4254] [,4255]
[1,] 1 0 0 0 0 1 0 0 0
[2,] 1 0 0 0 0 1 0 0 1
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[4,] 0 1 0 0 0 0 0 0 1
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[6,] 1 0 0 0 0 0 1 0 0
[7,] 1 1 1 0 0 0 1 1 0
[8,] 1 0 0 0 1 0 0 0 0
[9,] 1 0 1 0 0 0 0 0 0
[10,] 1 0 0 0 0 1 0 1 0
[,4256] [,4257] [,4258] [,4259] [,4260] [,4261] [,4262] [,4263] [,4264]
[1,] 0 0 0 0 0 1 0 0 0
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[4,] 1 0 0 0 0 0 0 1 1
[5,] 0 0 0 1 0 0 1 0 1
[6,] 0 0 1 0 0 1 1 1 0
[7,] 0 1 0 0 0 0 0 1 0
[8,] 0 0 0 0 0 0 0 0 0
[9,] 1 1 0 1 0 1 0 0 1
[10,] 1 0 0 1 0 0 1 0 0
[,4265] [,4266] [,4267] [,4268] [,4269] [,4270] [,4271] [,4272] [,4273]
[1,] 0 0 0 0 0 0 0 0 0
[2,] 0 0 0 0 0 0 1 1 0
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[4,] 0 0 1 0 0 1 0 1 0
[5,] 0 0 1 0 0 0 0 0 0
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[7,] 0 0 0 0 1 0 1 1 0
[8,] 0 1 1 0 1 0 0 1 0
[9,] 0 0 0 0 1 0 1 0 0
[10,] 0 0 1 0 0 0 1 0 1
[,4274] [,4275] [,4276] [,4277] [,4278] [,4279] [,4280] [,4281] [,4282]
[1,] 0 0 1 0 1 1 1 0 0
[2,] 0 0 0 0 0 1 0 0 1
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[4,] 0 0 0 0 0 1 0 0 0
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[7,] 0 1 0 1 1 1 1 1 1
[8,] 0 0 1 0 0 0 0 0 0
[9,] 1 0 0 0 0 0 0 0 0
[10,] 0 1 0 1 0 0 0 0 0
[,4283] [,4284] [,4285] [,4286] [,4287] [,4288] [,4289] [,4290] [,4291]
[1,] 0 0 1 0 1 0 1 0 0
[2,] 1 0 1 0 0 0 0 0 1
[3,] 1 0 0 0 0 0 1 1 0
[4,] 0 0 0 0 0 1 1 0 0
[5,] 0 0 0 0 0 0 0 1 0
[6,] 0 0 0 0 0 0 0 0 0
[7,] 1 0 0 0 0 0 0 0 1
[8,] 0 1 0 0 0 0 0 0 0
[9,] 0 0 0 0 0 0 0 0 0
[10,] 0 0 0 0 1 1 0 1 0
[,4292] [,4293] [,4294] [,4295] [,4296] [,4297] [,4298] [,4299] [,4300]
[1,] 0 0 1 1 0 0 0 1 0
[2,] 0 0 1 0 0 1 0 0 0
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[4,] 1 0 0 0 1 0 1 1 0
[5,] 0 0 0 1 0 0 0 0 0
[6,] 0 1 1 0 0 0 0 0 0
[7,] 0 0 0 1 1 0 0 1 0
[8,] 1 0 0 0 0 0 1 0 1
[9,] 0 1 0 0 0 1 0 1 1
[10,] 0 0 0 0 0 0 0 1 0
[,4301] [,4302] [,4303] [,4304] [,4305] [,4306] [,4307] [,4308] [,4309]
[1,] 1 1 0 0 0 0 1 0 1
[2,] 1 1 1 0 0 0 1 0 0
[3,] 0 1 0 0 1 1 1 1 0
[4,] 1 0 1 0 0 0 0 1 0
[5,] 0 1 0 0 1 1 0 1 1
[6,] 1 0 0 0 1 1 1 1 0
[7,] 0 0 0 0 0 0 1 0 0
[8,] 0 1 0 0 0 1 1 0 0
[9,] 0 0 0 1 0 0 0 0 0
[10,] 0 0 0 1 0 0 0 0 0
[,4310] [,4311] [,4312] [,4313] [,4314] [,4315] [,4316] [,4317] [,4318]
[1,] 0 0 0 0 0 1 1 0 0
[2,] 0 1 0 0 0 0 0 0 0
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[5,] 0 0 1 1 0 1 0 0 1
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[7,] 0 0 0 0 0 0 0 0 0
[8,] 0 0 0 1 0 1 0 0 0
[9,] 1 0 0 0 0 0 0 1 0
[10,] 0 0 0 0 0 1 0 0 1
[,4319] [,4320] [,4321] [,4322] [,4323] [,4324] [,4325] [,4326] [,4327]
[1,] 0 0 0 0 0 0 1 0 0
[2,] 0 0 1 0 1 0 0 1 0
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[4,] 0 0 0 0 0 0 0 0 0
[5,] 0 1 0 0 0 0 1 0 0
[6,] 0 0 0 0 1 0 0 1 1
[7,] 0 0 0 0 0 0 0 0 0
[8,] 0 0 0 0 1 1 1 0 0
[9,] 0 1 0 0 1 1 0 0 0
[10,] 1 0 0 1 0 0 0 0 0
[,4328] [,4329] [,4330] [,4331] [,4332] [,4333] [,4334] [,4335] [,4336]
[1,] 0 0 1 0 1 0 0 0 0
[2,] 0 0 0 0 0 0 0 1 0
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[4,] 0 0 1 0 0 0 0 0 1
[5,] 0 0 0 1 0 0 1 0 1
[6,] 1 1 0 0 0 0 0 0 0
[7,] 0 1 0 0 1 0 0 0 0
[8,] 1 0 0 1 0 0 0 0 1
[9,] 0 0 0 1 0 1 0 0 0
[10,] 0 0 1 0 0 0 0 0 1
[,4337] [,4338] [,4339] [,4340] [,4341] [,4342] [,4343] [,4344] [,4345]
[1,] 1 0 0 1 0 1 1 1 1
[2,] 0 1 0 0 1 1 0 1 0
[3,] 0 1 0 0 0 0 0 0 0
[4,] 1 0 0 0 0 1 0 0 1
[5,] 1 1 0 0 0 1 0 0 0
[6,] 0 1 0 0 0 0 0 0 0
[7,] 0 1 0 0 1 1 0 0 1
[8,] 1 0 0 0 0 0 0 0 1
[9,] 0 0 0 0 0 0 0 1 0
[10,] 0 1 1 0 0 1 0 0 0
[,4346] [,4347] [,4348] [,4349] [,4350] [,4351] [,4352] [,4353] [,4354]
[1,] 1 1 0 0 0 0 0 0 0
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[7,] 0 0 1 1 0 0 1 0 1
[8,] 0 1 0 0 0 0 1 0 0
[9,] 1 1 1 0 0 0 0 1 0
[10,] 0 1 0 0 0 0 0 0 1
[,4355] [,4356] [,4357] [,4358] [,4359] [,4360] [,4361] [,4362] [,4363]
[1,] 0 0 0 0 0 1 1 0 0
[2,] 0 1 0 0 0 0 0 1 0
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[7,] 0 0 0 0 0 0 0 1 0
[8,] 1 1 0 0 0 1 1 0 1
[9,] 1 0 0 0 0 1 0 1 0
[10,] 0 0 0 1 0 0 0 1 0
[,4364] [,4365] [,4366] [,4367] [,4368] [,4369] [,4370] [,4371] [,4372]
[1,] 0 1 0 0 0 0 1 0 1
[2,] 0 0 0 0 0 1 1 1 0
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[4,] 0 0 1 0 0 0 1 0 0
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[6,] 0 0 0 1 0 1 0 1 0
[7,] 0 0 0 1 0 0 1 0 0
[8,] 0 0 0 0 0 1 1 1 1
[9,] 0 0 0 0 1 1 1 0 0
[10,] 0 0 0 0 1 0 0 0 0
[,4373] [,4374] [,4375] [,4376] [,4377] [,4378] [,4379] [,4380] [,4381]
[1,] 1 0 0 0 1 0 1 0 0
[2,] 0 0 0 1 0 0 0 1 0
[3,] 1 1 1 0 1 0 0 1 1
[4,] 0 0 0 0 0 0 0 0 0
[5,] 1 1 0 1 1 0 0 0 0
[6,] 0 1 0 0 0 0 1 1 1
[7,] 0 0 1 0 0 0 0 1 0
[8,] 0 0 0 0 1 0 0 0 0
[9,] 0 0 0 1 1 0 1 0 1
[10,] 0 0 0 1 0 0 0 0 0
[,4382] [,4383] [,4384] [,4385] [,4386] [,4387] [,4388] [,4389] [,4390]
[1,] 0 0 0 0 0 1 0 0 0
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[7,] 0 0 0 1 1 1 0 0 0
[8,] 0 0 0 0 0 0 1 0 0
[9,] 1 0 1 0 0 0 1 1 0
[10,] 0 0 1 0 0 0 1 0 0
[,4391] [,4392] [,4393] [,4394] [,4395] [,4396] [,4397] [,4398] [,4399]
[1,] 0 0 1 0 0 0 1 1 0
[2,] 1 0 0 0 1 1 0 0 1
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[7,] 0 0 0 0 1 0 0 1 0
[8,] 1 0 0 0 0 0 1 0 0
[9,] 0 0 0 0 1 1 0 0 1
[10,] 1 0 1 0 0 0 0 0 0
[,4400] [,4401] [,4402] [,4403] [,4404] [,4405] [,4406] [,4407] [,4408]
[1,] 0 1 1 0 0 1 0 1 0
[2,] 1 1 0 1 0 0 0 0 1
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[6,] 1 0 0 1 1 0 0 1 0
[7,] 0 1 0 0 1 1 1 0 0
[8,] 0 1 0 1 1 0 0 1 0
[9,] 0 0 1 1 0 1 0 0 0
[10,] 1 1 0 1 0 0 1 0 0
[,4409] [,4410] [,4411] [,4412] [,4413] [,4414] [,4415] [,4416] [,4417]
[1,] 0 0 1 0 0 0 0 0 1
[2,] 1 0 0 0 0 1 0 0 0
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[6,] 0 1 0 1 0 0 1 0 0
[7,] 0 0 1 0 0 0 0 1 1
[8,] 0 0 0 0 0 0 0 0 0
[9,] 1 0 0 0 1 0 0 1 1
[10,] 0 0 0 0 0 0 1 1 0
[,4418] [,4419] [,4420] [,4421] [,4422] [,4423] [,4424] [,4425] [,4426]
[1,] 1 1 0 0 0 1 0 0 0
[2,] 1 0 0 1 1 0 1 0 0
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[4,] 0 0 1 0 0 0 1 1 0
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[6,] 0 0 1 0 0 0 0 0 1
[7,] 0 1 0 1 0 1 0 1 0
[8,] 0 0 1 0 0 1 0 0 0
[9,] 0 0 0 0 0 0 0 0 0
[10,] 0 0 0 0 0 0 0 0 0
[,4427] [,4428] [,4429] [,4430] [,4431] [,4432] [,4433] [,4434] [,4435]
[1,] 0 0 0 0 1 1 0 0 0
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[4,] 0 1 0 0 0 0 0 1 0
[5,] 0 0 0 0 0 0 1 0 0
[6,] 0 1 0 0 1 0 0 1 1
[7,] 0 0 0 1 0 1 1 0 0
[8,] 1 0 0 0 1 1 1 0 0
[9,] 0 0 0 1 0 0 0 0 1
[10,] 1 0 0 0 0 0 0 1 0
[,4436] [,4437] [,4438] [,4439] [,4440] [,4441] [,4442] [,4443] [,4444]
[1,] 1 0 1 0 0 1 1 0 1
[2,] 0 0 0 0 0 0 0 0 1
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[7,] 0 0 1 0 1 1 1 0 0
[8,] 0 0 1 0 0 0 0 1 0
[9,] 0 0 0 0 0 0 0 1 0
[10,] 0 0 0 0 0 0 0 1 0
[,4445] [,4446] [,4447] [,4448] [,4449] [,4450] [,4451] [,4452] [,4453]
[1,] 0 0 1 0 0 0 0 0 0
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[8,] 0 0 0 0 0 0 1 1 0
[9,] 0 1 0 0 0 0 0 1 0
[10,] 0 0 0 0 0 0 0 0 0
[,4454] [,4455] [,4456] [,4457] [,4458] [,4459] [,4460] [,4461] [,4462]
[1,] 0 0 0 0 1 0 0 0 0
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[8,] 0 0 0 0 1 0 0 1 0
[9,] 0 1 1 0 1 0 0 0 0
[10,] 0 0 1 1 0 0 0 0 1
[,4463] [,4464] [,4465] [,4466] [,4467] [,4468] [,4469] [,4470] [,4471]
[1,] 1 1 1 1 0 0 0 0 1
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[10,] 0 1 0 0 0 0 1 1 0
[,4472] [,4473] [,4474] [,4475] [,4476] [,4477] [,4478] [,4479] [,4480]
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[10,] 1 0 0 0 1 0 0 0 0
[,4481] [,4482] [,4483] [,4484] [,4485] [,4486] [,4487] [,4488] [,4489]
[1,] 0 0 1 0 0 0 0 0 0
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[7,] 1 0 0 1 0 0 1 1 1
[8,] 0 0 0 1 1 0 0 0 0
[9,] 0 1 0 0 0 1 0 1 0
[10,] 0 0 0 0 0 1 0 0 0
[,4490] [,4491] [,4492] [,4493] [,4494] [,4495] [,4496] [,4497] [,4498]
[1,] 0 0 0 0 0 0 0 1 0
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[10,] 0 1 0 0 0 1 0 1 0
[,4499] [,4500] [,4501] [,4502] [,4503] [,4504] [,4505] [,4506] [,4507]
[1,] 0 1 1 0 1 0 0 0 0
[2,] 0 0 1 0 0 0 0 0 0
[3,] 0 1 1 0 0 0 0 0 0
[4,] 1 0 0 0 0 0 0 1 1
[5,] 0 0 0 1 0 0 0 1 0
[6,] 0 0 0 1 1 0 1 0 0
[7,] 0 0 0 0 1 0 0 1 0
[8,] 1 1 0 0 1 1 0 1 1
[9,] 0 1 0 0 1 0 0 1 1
[10,] 0 0 0 0 0 0 0 1 1
[,4508] [,4509] [,4510] [,4511] [,4512] [,4513] [,4514] [,4515] [,4516]
[1,] 1 0 0 0 0 0 1 0 0
[2,] 0 0 0 0 0 1 0 0 0
[3,] 0 0 0 0 0 0 1 1 0
[4,] 0 0 0 0 1 1 0 0 0
[5,] 1 0 0 0 0 0 0 0 0
[6,] 0 0 0 0 1 0 0 1 0
[7,] 0 0 0 0 0 0 0 0 0
[8,] 0 0 0 0 0 1 1 1 0
[9,] 0 0 0 0 0 0 0 0 0
[10,] 1 1 0 1 1 0 0 0 0
[,4517] [,4518] [,4519] [,4520] [,4521] [,4522] [,4523] [,4524] [,4525]
[1,] 0 0 1 0 1 0 0 1 0
[2,] 0 0 0 0 1 0 0 1 0
[3,] 0 0 1 0 0 1 0 0 1
[4,] 0 0 0 1 0 0 0 0 1
[5,] 1 0 1 0 1 1 0 0 0
[6,] 1 1 0 0 1 0 0 0 1
[7,] 0 1 1 0 0 1 0 1 0
[8,] 0 0 1 0 0 0 1 1 0
[9,] 1 1 0 0 0 0 0 0 1
[10,] 0 1 0 0 0 0 1 1 1
[,4526] [,4527] [,4528] [,4529] [,4530] [,4531] [,4532] [,4533] [,4534]
[1,] 0 1 1 0 0 0 0 1 0
[2,] 0 1 0 0 1 1 0 0 0
[3,] 0 0 0 0 1 0 1 0 1
[4,] 1 1 0 0 1 0 1 1 1
[5,] 0 0 0 0 1 0 1 0 0
[6,] 0 0 0 0 1 0 0 0 1
[7,] 1 1 0 0 0 0 0 0 1
[8,] 1 1 0 0 0 1 0 0 0
[9,] 0 1 0 0 1 0 1 0 0
[10,] 0 0 0 1 0 0 0 0 0
[,4535] [,4536] [,4537] [,4538] [,4539] [,4540] [,4541] [,4542] [,4543]
[1,] 0 0 0 0 0 1 1 1 0
[2,] 0 0 0 0 0 0 0 0 1
[3,] 0 0 0 0 0 1 1 1 1
[4,] 0 0 0 0 0 0 0 1 1
[5,] 1 0 0 0 0 0 1 1 0
[6,] 0 0 0 0 0 0 1 0 1
[7,] 0 1 0 0 0 1 0 0 0
[8,] 0 0 0 0 0 0 0 0 0
[9,] 1 0 1 1 0 1 0 0 1
[10,] 0 1 0 0 0 1 0 0 0
[,4544] [,4545] [,4546] [,4547] [,4548] [,4549] [,4550] [,4551] [,4552]
[1,] 1 0 0 1 0 0 0 0 1
[2,] 0 0 0 0 0 0 1 0 0
[3,] 0 0 0 0 0 1 0 1 0
[4,] 0 0 0 0 0 0 0 0 0
[5,] 0 1 0 0 1 0 1 0 1
[6,] 0 0 0 1 1 0 1 0 0
[7,] 0 0 1 0 1 0 0 1 1
[8,] 0 0 0 0 0 1 0 0 1
[9,] 0 0 0 1 0 0 0 0 1
[10,] 1 1 0 0 1 1 0 0 0
[,4553] [,4554] [,4555] [,4556] [,4557] [,4558] [,4559] [,4560] [,4561]
[1,] 1 0 1 0 0 0 0 0 0
[2,] 0 0 0 0 0 1 0 0 1
[3,] 0 0 0 0 0 0 0 0 1
[4,] 1 1 1 0 0 0 1 0 1
[5,] 1 1 0 0 0 0 0 0 0
[6,] 1 0 0 1 0 0 1 0 0
[7,] 0 0 1 1 0 0 1 0 0
[8,] 0 0 0 1 0 0 0 0 1
[9,] 0 1 0 0 1 0 1 1 0
[10,] 1 0 0 0 1 1 0 0 0
[,4562] [,4563] [,4564] [,4565] [,4566] [,4567] [,4568] [,4569] [,4570]
[1,] 1 1 0 0 0 0 1 1 0
[2,] 1 0 1 0 0 0 0 0 1
[3,] 0 0 0 0 0 0 1 0 0
[4,] 0 0 1 0 0 0 0 0 0
[5,] 0 0 0 1 0 0 1 1 0
[6,] 1 0 0 0 0 1 0 1 0
[7,] 1 0 0 0 0 0 0 1 1
[8,] 0 1 0 0 0 0 0 0 0
[9,] 0 0 0 0 0 1 0 0 1
[10,] 0 0 0 1 0 0 0 0 0
[,4571] [,4572] [,4573] [,4574] [,4575] [,4576] [,4577] [,4578] [,4579]
[1,] 0 0 0 0 0 0 1 0 0
[2,] 0 0 0 0 0 0 0 0 0
[3,] 1 0 0 1 0 1 1 1 0
[4,] 1 1 0 0 1 1 0 0 1
[5,] 0 1 0 0 0 0 0 1 0
[6,] 0 0 1 0 1 0 0 0 0
[7,] 0 1 0 0 0 1 1 1 0
[8,] 0 0 0 0 1 0 0 0 0
[9,] 1 0 0 0 1 0 0 0 1
[10,] 0 0 0 1 0 0 0 1 0
[,4580] [,4581] [,4582] [,4583] [,4584] [,4585] [,4586] [,4587] [,4588]
[1,] 1 0 0 0 0 0 0 1 0
[2,] 0 1 0 0 1 0 0 1 0
[3,] 0 0 0 0 1 0 0 0 0
[4,] 0 0 0 1 1 0 1 1 0
[5,] 0 0 0 0 0 0 0 0 0
[6,] 0 0 0 0 0 0 1 0 1
[7,] 1 0 0 0 0 0 0 0 1
[8,] 1 0 0 0 0 0 0 0 0
[9,] 0 0 1 0 0 0 1 0 1
[10,] 0 0 1 1 0 1 0 0 0
[,4589] [,4590] [,4591] [,4592] [,4593] [,4594] [,4595] [,4596] [,4597]
[1,] 0 0 0 0 1 1 0 0 0
[2,] 0 0 1 0 1 0 0 0 0
[3,] 0 1 0 0 0 0 0 0 0
[4,] 0 1 1 0 0 0 0 1 1
[5,] 0 0 0 0 0 0 0 0 0
[6,] 0 0 0 0 0 0 0 0 0
[7,] 0 1 0 0 0 1 0 0 0
[8,] 0 0 1 1 0 0 1 0 0
[9,] 0 0 1 0 1 1 0 0 0
[10,] 1 0 0 1 0 0 0 1 0
[,4598] [,4599] [,4600] [,4601] [,4602] [,4603] [,4604] [,4605] [,4606]
[1,] 1 0 0 0 0 1 1 0 1
[2,] 0 0 0 0 0 0 0 0 0
[3,] 0 0 1 0 0 0 0 0 0
[4,] 1 0 1 1 0 1 0 0 1
[5,] 1 0 0 0 0 0 0 1 1
[6,] 0 1 1 1 0 0 0 1 0
[7,] 1 0 0 0 0 0 0 0 0
[8,] 0 0 0 0 0 0 0 0 0
[9,] 0 0 0 0 0 0 0 0 0
[10,] 0 1 0 0 0 0 0 0 1
[,4607] [,4608] [,4609] [,4610] [,4611] [,4612] [,4613] [,4614] [,4615]
[1,] 0 0 0 1 0 0 0 1 1
[2,] 0 0 1 0 0 0 1 1 0
[3,] 1 0 0 1 0 1 0 0 0
[4,] 0 0 0 0 0 0 0 1 0
[5,] 1 0 0 0 0 0 0 0 1
[6,] 1 0 1 0 1 0 0 0 1
[7,] 0 0 0 0 0 0 1 0 1
[8,] 1 1 0 0 0 1 0 0 0
[9,] 0 0 1 0 1 1 0 0 0
[10,] 1 1 0 0 0 0 0 1 0
[,4616] [,4617] [,4618] [,4619] [,4620] [,4621] [,4622] [,4623] [,4624]
[1,] 0 0 0 0 0 0 0 0 1
[2,] 0 0 1 0 0 1 1 0 0
[3,] 0 0 0 0 0 0 0 0 0
[4,] 1 0 1 1 0 0 0 0 1
[5,] 0 0 0 1 0 0 0 0 0
[6,] 0 0 0 1 1 0 1 0 0
[7,] 0 0 0 1 1 0 0 0 0
[8,] 0 0 1 1 1 0 1 0 0
[9,] 0 0 0 0 0 0 1 0 1
[10,] 0 0 0 0 1 1 0 0 0
[,4625] [,4626] [,4627] [,4628] [,4629] [,4630] [,4631] [,4632] [,4633]
[1,] 0 0 1 0 1 0 1 1 1
[2,] 0 0 1 0 1 0 1 0 0
[3,] 0 1 0 1 1 0 0 0 0
[4,] 0 0 0 1 0 0 1 1 0
[5,] 0 1 0 0 0 0 1 1 0
[6,] 0 0 1 0 0 1 1 0 0
[7,] 0 0 0 0 1 0 0 0 1
[8,] 1 0 0 0 0 0 0 0 0
[9,] 0 0 1 0 0 0 0 1 1
[10,] 1 0 0 0 0 0 1 0 1
[,4634] [,4635] [,4636] [,4637] [,4638] [,4639] [,4640] [,4641] [,4642]
[1,] 0 0 0 0 1 0 1 1 0
[2,] 0 0 0 0 0 0 1 1 1
[3,] 0 0 0 0 0 0 0 0 1
[4,] 1 0 0 0 0 0 0 1 0
[5,] 0 0 1 0 0 0 0 1 0
[6,] 0 1 1 0 0 1 1 0 0
[7,] 0 0 0 0 0 1 0 0 1
[8,] 0 0 0 0 0 1 1 0 0
[9,] 0 0 0 1 0 0 0 1 0
[10,] 0 1 0 0 0 1 0 0 0
[,4643] [,4644] [,4645] [,4646] [,4647] [,4648] [,4649] [,4650] [,4651]
[1,] 0 0 0 0 0 0 1 0 0
[2,] 0 0 0 0 0 1 1 0 1
[3,] 0 1 1 1 0 1 0 1 1
[4,] 0 0 0 0 0 0 0 0 0
[5,] 0 1 0 0 1 0 0 0 0
[6,] 0 1 0 1 0 1 0 0 0
[7,] 0 0 1 1 1 1 0 1 0
[8,] 0 0 0 0 0 1 1 0 1
[9,] 0 0 0 1 0 0 0 0 0
[10,] 0 0 0 1 0 0 1 1 1
[,4652] [,4653] [,4654] [,4655] [,4656] [,4657] [,4658] [,4659] [,4660]
[1,] 0 0 0 0 1 0 0 1 0
[2,] 0 0 1 0 1 1 1 0 0
[3,] 1 0 1 0 1 1 1 1 0
[4,] 0 0 0 0 0 1 0 0 0
[5,] 0 0 0 0 0 1 0 0 1
[6,] 0 1 1 0 1 1 0 0 0
[7,] 0 0 1 0 0 0 1 0 1
[8,] 0 1 0 0 0 1 0 1 1
[9,] 0 1 0 1 1 0 0 1 0
[10,] 0 1 1 0 0 0 1 0 0
[,4661] [,4662] [,4663] [,4664] [,4665] [,4666] [,4667] [,4668] [,4669]
[1,] 0 1 0 0 0 0 1 0 0
[2,] 1 1 0 0 1 0 0 0 1
[3,] 1 0 1 0 0 0 0 0 1
[4,] 0 0 0 1 0 0 0 0 0
[5,] 0 0 1 0 0 0 1 0 1
[6,] 0 0 1 1 1 0 0 1 0
[7,] 1 0 0 1 1 0 0 0 0
[8,] 0 0 1 1 0 0 0 0 1
[9,] 0 0 0 0 0 0 0 0 0
[10,] 0 0 0 0 1 0 0 0 0
[,4670] [,4671] [,4672] [,4673] [,4674] [,4675] [,4676] [,4677] [,4678]
[1,] 0 0 1 0 0 0 0 0 1
[2,] 0 0 0 1 1 0 0 0 0
[3,] 0 0 1 0 0 0 0 0 0
[4,] 0 1 1 0 0 0 1 1 0
[5,] 1 0 0 0 0 1 0 0 0
[6,] 1 0 0 0 1 1 1 0 0
[7,] 1 0 0 0 1 0 0 0 0
[8,] 0 0 0 0 0 1 1 0 1
[9,] 0 0 0 0 0 1 0 0 0
[10,] 1 1 0 0 0 0 0 0 0
[,4679] [,4680] [,4681] [,4682] [,4683] [,4684] [,4685] [,4686] [,4687]
[1,] 0 0 0 1 1 0 1 0 1
[2,] 1 1 0 0 1 1 0 1 0
[3,] 1 0 0 0 1 0 0 1 0
[4,] 0 0 0 0 0 0 0 0 0
[5,] 0 0 1 0 0 0 1 1 0
[6,] 0 0 0 0 0 0 0 1 0
[7,] 1 1 0 0 0 0 0 0 0
[8,] 1 0 0 0 1 1 1 0 0
[9,] 0 0 1 0 0 0 0 0 0
[10,] 1 1 0 1 1 0 0 1 0
[,4688] [,4689] [,4690] [,4691] [,4692] [,4693] [,4694] [,4695] [,4696]
[1,] 1 0 0 0 0 0 0 0 1
[2,] 0 0 1 0 1 0 0 1 0
[3,] 1 0 1 0 0 1 0 1 0
[4,] 0 1 0 0 1 1 0 0 0
[5,] 0 1 0 0 1 1 0 0 0
[6,] 0 0 0 0 0 0 1 0 0
[7,] 0 0 0 1 1 0 0 0 0
[8,] 0 0 0 1 0 0 0 0 1
[9,] 1 0 0 0 0 0 0 1 0
[10,] 0 0 0 0 0 1 1 1 0
[,4697] [,4698] [,4699] [,4700] [,4701] [,4702] [,4703] [,4704] [,4705]
[1,] 0 0 1 0 0 0 1 0 0
[2,] 0 1 0 0 0 0 0 1 0
[3,] 0 0 0 0 0 0 0 0 0
[4,] 0 0 0 0 0 0 1 0 0
[5,] 1 0 1 1 1 0 0 0 0
[6,] 0 0 1 1 0 0 0 0 1
[7,] 0 0 0 0 1 1 1 0 0
[8,] 0 0 0 0 1 1 1 0 0
[9,] 0 0 0 0 0 1 0 0 1
[10,] 1 0 0 0 0 0 0 0 0
[,4706] [,4707] [,4708] [,4709] [,4710] [,4711] [,4712] [,4713] [,4714]
[1,] 0 1 0 0 0 1 0 1 0
[2,] 0 0 0 0 0 1 1 0 0
[3,] 0 1 1 1 0 0 0 0 0
[4,] 1 0 0 0 0 0 1 1 1
[5,] 0 0 0 0 1 0 0 1 1
[6,] 0 0 0 1 0 0 1 1 0
[7,] 0 0 0 1 1 0 0 1 0
[8,] 1 1 1 0 0 1 0 0 0
[9,] 0 1 0 0 0 0 0 0 0
[10,] 0 0 0 0 0 1 0 0 0
[,4715] [,4716] [,4717] [,4718] [,4719] [,4720] [,4721] [,4722] [,4723]
[1,] 0 1 0 0 0 0 0 0 0
[2,] 0 0 0 0 0 0 0 0 0
[3,] 1 1 0 1 0 0 0 0 0
[4,] 0 0 0 0 0 0 1 0 0
[5,] 0 0 1 0 1 0 0 0 0
[6,] 0 0 0 0 0 0 0 0 0
[7,] 0 1 0 0 0 0 1 0 1
[8,] 0 1 0 0 0 0 1 1 1
[9,] 0 0 0 0 0 1 0 1 0
[10,] 0 1 0 0 0 0 0 0 1
[,4724] [,4725] [,4726] [,4727] [,4728] [,4729] [,4730] [,4731] [,4732]
[1,] 0 1 0 0 0 0 0 1 1
[2,] 0 1 0 0 0 1 0 0 1
[3,] 0 0 0 0 0 0 0 1 1
[4,] 0 0 0 0 0 0 0 1 0
[5,] 0 0 1 1 1 0 1 0 0
[6,] 1 1 1 0 0 0 0 0 0
[7,] 0 1 0 0 0 0 1 0 1
[8,] 1 1 0 0 0 0 0 1 0
[9,] 0 1 1 1 0 1 0 0 0
[10,] 0 0 0 0 1 0 0 0 1
[,4733] [,4734] [,4735] [,4736] [,4737] [,4738] [,4739] [,4740] [,4741]
[1,] 1 0 0 0 0 0 0 0 0
[2,] 1 0 0 0 0 1 0 0 0
[3,] 1 0 0 0 1 1 0 1 0
[4,] 0 0 1 0 0 0 0 0 0
[5,] 1 1 0 0 0 1 1 0 0
[6,] 1 1 1 0 0 1 0 1 0
[7,] 1 0 0 0 0 0 0 0 0
[8,] 0 0 0 0 0 1 1 0 1
[9,] 0 0 0 0 0 0 1 0 0
[10,] 0 0 0 0 0 0 0 0 1
[,4742] [,4743] [,4744] [,4745] [,4746] [,4747] [,4748] [,4749] [,4750]
[1,] 0 1 0 0 0 0 1 0 0
[2,] 0 0 0 0 1 0 0 0 0
[3,] 0 0 0 0 1 1 1 0 1
[4,] 1 0 0 0 0 0 0 0 0
[5,] 0 0 0 1 0 0 0 0 0
[6,] 0 1 1 0 1 0 1 0 1
[7,] 0 1 0 0 0 1 0 1 0
[8,] 1 0 0 0 0 0 0 1 0
[9,] 1 0 0 0 1 0 1 0 1
[10,] 0 0 1 0 1 1 0 1 0
[,4751] [,4752] [,4753] [,4754] [,4755] [,4756] [,4757] [,4758] [,4759]
[1,] 0 1 0 0 0 0 0 1 0
[2,] 1 1 0 0 0 0 0 0 0
[3,] 0 1 1 0 0 1 0 0 0
[4,] 0 1 0 0 1 0 0 1 0
[5,] 0 1 0 0 0 0 0 1 0
[6,] 0 1 0 0 0 0 0 0 1
[7,] 0 0 1 1 0 0 0 0 0
[8,] 0 1 0 1 0 1 0 0 0
[9,] 0 0 0 0 0 0 0 0 1
[10,] 0 1 0 0 0 1 1 1 0
[,4760] [,4761] [,4762] [,4763] [,4764] [,4765] [,4766] [,4767] [,4768]
[1,] 0 0 0 0 0 1 1 0 1
[2,] 0 0 0 1 1 0 0 1 0
[3,] 0 1 0 1 0 0 0 0 0
[4,] 1 0 0 1 1 0 0 0 0
[5,] 1 1 0 0 0 1 1 1 1
[6,] 0 0 1 0 0 0 0 0 0
[7,] 0 0 1 1 0 0 0 1 1
[8,] 0 0 1 0 0 0 0 0 0
[9,] 0 0 1 0 0 0 1 1 1
[10,] 0 0 1 1 1 0 1 1 0
[,4769] [,4770] [,4771] [,4772] [,4773] [,4774] [,4775] [,4776] [,4777]
[1,] 0 1 0 0 0 1 1 0 0
[2,] 1 1 0 1 0 0 0 0 1
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[6,] 0 0 0 0 0 1 1 0 1
[7,] 0 0 0 1 0 0 0 0 0
[8,] 1 0 1 1 1 0 0 1 0
[9,] 0 0 0 1 0 0 0 0 0
[10,] 0 1 0 0 0 0 0 0 0
[,4778] [,4779] [,4780] [,4781] [,4782] [,4783] [,4784] [,4785] [,4786]
[1,] 0 0 0 0 0 0 1 1 0
[2,] 1 0 0 0 0 1 0 1 0
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[6,] 1 0 1 0 1 1 0 1 0
[7,] 0 1 0 0 0 0 0 1 0
[8,] 1 0 1 0 1 1 0 0 0
[9,] 1 1 1 0 0 0 0 0 0
[10,] 1 0 0 1 1 0 1 0 0
[,4787] [,4788] [,4789] [,4790] [,4791] [,4792] [,4793] [,4794] [,4795]
[1,] 1 0 0 0 0 0 1 0 0
[2,] 0 0 1 1 0 0 0 1 1
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[6,] 1 0 0 0 0 0 0 0 0
[7,] 0 1 0 0 0 0 1 0 0
[8,] 0 1 0 0 1 1 1 1 0
[9,] 1 1 0 0 1 0 0 1 0
[10,] 0 0 1 0 0 1 1 1 0
[,4796] [,4797] [,4798] [,4799] [,4800] [,4801] [,4802] [,4803] [,4804]
[1,] 0 0 0 0 0 0 0 1 0
[2,] 0 0 0 0 0 0 0 0 0
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[4,] 0 1 0 0 1 0 1 1 0
[5,] 0 0 1 1 0 1 0 1 0
[6,] 1 0 1 0 0 0 1 0 0
[7,] 0 0 0 0 0 0 0 0 0
[8,] 1 0 0 0 0 0 0 0 1
[9,] 1 0 1 0 1 1 0 0 1
[10,] 1 0 0 0 0 0 0 0 0
[,4805] [,4806] [,4807] [,4808] [,4809] [,4810] [,4811] [,4812] [,4813]
[1,] 0 1 0 1 0 0 0 0 1
[2,] 1 0 0 0 0 0 0 0 0
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[6,] 0 1 1 0 0 0 1 1 0
[7,] 1 1 0 0 0 0 0 0 1
[8,] 0 1 1 1 0 1 1 0 0
[9,] 0 0 0 0 0 1 0 1 0
[10,] 0 1 1 1 1 1 0 1 1
[,4814] [,4815] [,4816] [,4817] [,4818] [,4819] [,4820] [,4821] [,4822]
[1,] 0 1 0 0 0 0 0 0 0
[2,] 0 1 0 0 1 0 1 0 0
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[6,] 0 0 0 0 0 1 0 1 1
[7,] 0 0 0 0 1 0 0 0 0
[8,] 0 0 0 0 0 0 1 0 1
[9,] 0 1 0 0 1 0 0 0 0
[10,] 0 1 0 0 0 1 0 0 0
[,4823] [,4824] [,4825] [,4826] [,4827] [,4828] [,4829] [,4830] [,4831]
[1,] 0 0 0 1 0 0 0 0 0
[2,] 0 1 1 1 0 0 0 0 0
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[7,] 0 1 0 0 0 1 0 1 0
[8,] 0 0 0 1 0 0 0 0 0
[9,] 0 1 0 0 0 1 0 0 1
[10,] 0 0 0 0 0 0 1 0 0
[,4832] [,4833] [,4834] [,4835] [,4836] [,4837] [,4838] [,4839] [,4840]
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[7,] 1 0 0 0 0 0 1 0 0
[8,] 0 0 0 0 1 0 0 0 1
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[10,] 1 0 1 1 0 1 0 0 1
[,4841] [,4842] [,4843] [,4844] [,4845] [,4846] [,4847] [,4848] [,4849]
[1,] 0 1 0 1 1 0 0 0 1
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[7,] 0 0 0 0 1 0 0 0 0
[8,] 0 1 0 1 1 1 1 0 0
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[10,] 0 0 0 0 1 1 0 0 1
[,4850] [,4851] [,4852] [,4853] [,4854] [,4855] [,4856] [,4857] [,4858]
[1,] 0 0 0 0 0 1 0 0 0
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[7,] 0 0 0 0 1 1 0 1 0
[8,] 0 1 0 0 0 0 0 0 0
[9,] 0 1 0 0 0 1 0 0 0
[10,] 0 0 0 0 0 0 0 1 0
[,4859] [,4860] [,4861] [,4862] [,4863] [,4864] [,4865] [,4866] [,4867]
[1,] 1 0 0 0 0 0 0 1 0
[2,] 0 1 0 1 0 0 1 0 0
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[7,] 1 0 0 0 0 0 1 0 0
[8,] 0 0 0 0 1 0 0 1 0
[9,] 0 1 0 1 0 1 0 0 1
[10,] 1 1 1 1 0 1 0 0 0
[,4868] [,4869] [,4870] [,4871] [,4872] [,4873] [,4874] [,4875] [,4876]
[1,] 0 0 1 0 0 1 0 1 0
[2,] 0 1 0 0 1 0 0 0 0
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[4,] 0 1 0 1 0 0 0 0 1
[5,] 1 0 0 0 0 0 0 0 0
[6,] 0 0 1 0 1 1 0 0 0
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[10,] 0 0 0 0 0 0 0 0 0
[,4877] [,4878] [,4879] [,4880] [,4881] [,4882] [,4883] [,4884] [,4885]
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[6,] 0 0 0 0 0 1 0 1 1
[7,] 0 0 0 0 1 0 0 1 0
[8,] 0 0 0 0 1 1 0 0 1
[9,] 1 1 0 0 0 0 0 1 0
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[,4886] [,4887] [,4888] [,4889] [,4890] [,4891] [,4892] [,4893] [,4894]
[1,] 1 0 0 0 1 1 0 1 0
[2,] 0 0 1 1 1 1 0 0 1
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[4,] 0 0 1 1 0 1 0 1 0
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[6,] 0 0 0 0 0 0 1 0 0
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[8,] 1 1 0 0 0 0 0 0 1
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[10,] 0 0 1 0 1 0 0 0 1
[,4895] [,4896] [,4897] [,4898] [,4899] [,4900] [,4901] [,4902] [,4903]
[1,] 1 0 1 0 0 0 0 0 1
[2,] 0 0 0 0 0 0 1 0 1
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[4,] 1 0 0 0 1 1 1 1 1
[5,] 0 0 1 0 0 1 0 1 0
[6,] 1 1 0 0 1 0 1 0 0
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[8,] 1 0 0 0 1 1 0 1 0
[9,] 0 0 1 1 0 1 0 0 1
[10,] 0 1 0 0 0 0 0 1 1
[,4904] [,4905] [,4906] [,4907] [,4908] [,4909] [,4910] [,4911] [,4912]
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[8,] 0 0 0 1 1 0 0 1 1
[9,] 1 1 0 0 1 0 1 0 0
[10,] 1 0 1 1 0 0 0 0 1
[,4913] [,4914] [,4915] [,4916] [,4917] [,4918] [,4919] [,4920] [,4921]
[1,] 0 1 0 0 0 0 1 0 0
[2,] 1 1 0 0 1 0 0 0 0
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[8,] 0 0 1 0 1 1 0 0 1
[9,] 0 1 0 0 0 0 0 0 0
[10,] 1 0 0 0 1 0 0 0 0
[,4922] [,4923] [,4924] [,4925] [,4926] [,4927] [,4928] [,4929] [,4930]
[1,] 0 0 1 0 0 0 0 0 0
[2,] 0 1 1 0 0 0 0 1 0
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[,4931] [,4932] [,4933] [,4934] [,4935] [,4936] [,4937] [,4938] [,4939]
[1,] 1 0 1 0 0 0 0 0 0
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[,4940] [,4941] [,4942] [,4943] [,4944] [,4945] [,4946] [,4947] [,4948]
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[,4949] [,4950] [,4951] [,4952] [,4953] [,4954] [,4955] [,4956] [,4957]
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[7,] 0 0 1 0 1 0 0 1 0
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[9,] 1 0 1 1 1 0 0 0 1
[10,] 0 0 0 0 1 0 0 1 0
[,4958] [,4959] [,4960] [,4961] [,4962] [,4963] [,4964] [,4965] [,4966]
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[10,] 1 1 0 0 0 0 0 0 0
[,4967] [,4968] [,4969] [,4970] [,4971] [,4972] [,4973] [,4974] [,4975]
[1,] 0 0 1 1 0 1 0 0 1
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[,4976] [,4977] [,4978] [,4979] [,4980] [,4981] [,4982] [,4983] [,4984]
[1,] 0 1 1 0 0 0 0 1 0
[2,] 0 0 1 1 1 0 0 1 0
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[10,] 0 0 0 0 0 1 0 0 1
[,4985] [,4986] [,4987] [,4988] [,4989] [,4990] [,4991] [,4992] [,4993]
[1,] 0 0 0 1 0 0 1 0 0
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[5,] 0 1 0 0 0 1 0 0 0
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[7,] 0 1 0 0 1 0 0 0 0
[8,] 0 0 0 0 0 1 1 0 1
[9,] 1 0 1 1 0 0 0 0 0
[10,] 0 0 0 0 1 0 0 0 0
[,4994] [,4995] [,4996] [,4997] [,4998] [,4999] [,5000] [,5001] [,5002]
[1,] 0 1 0 0 0 1 0 0 0
[2,] 0 0 0 1 1 0 0 1 0
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[7,] 1 0 1 0 1 1 1 0 0
[8,] 1 0 0 1 0 0 1 0 0
[9,] 0 0 0 0 0 0 0 0 0
[10,] 0 0 0 0 0 0 0 0 1
[,5003] [,5004] [,5005] [,5006] [,5007] [,5008] [,5009] [,5010] [,5011]
[1,] 1 0 0 1 0 0 0 1 1
[2,] 0 0 1 0 0 0 0 0 0
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[5,] 1 1 0 0 0 0 1 1 1
[6,] 0 0 1 1 0 0 0 1 0
[7,] 0 0 0 0 0 0 1 0 0
[8,] 1 0 1 0 0 1 0 0 0
[9,] 0 0 0 0 1 1 1 0 0
[10,] 1 0 0 0 0 0 0 0 0
[,5012] [,5013] [,5014] [,5015] [,5016] [,5017] [,5018] [,5019] [,5020]
[1,] 0 0 0 0 0 0 0 0 0
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[7,] 1 0 1 0 0 0 0 0 0
[8,] 0 0 0 0 0 0 1 0 0
[9,] 1 1 0 1 0 0 1 0 0
[10,] 0 0 0 0 0 0 1 0 1
[,5021] [,5022] [,5023] [,5024] [,5025] [,5026] [,5027] [,5028] [,5029]
[1,] 0 0 1 0 0 0 1 0 1
[2,] 0 0 1 0 1 0 1 1 0
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[7,] 0 0 0 0 0 0 0 0 0
[8,] 0 1 0 1 1 0 0 0 0
[9,] 1 0 0 1 0 0 0 1 0
[10,] 1 1 1 0 0 0 0 1 1
[,5030] [,5031] [,5032] [,5033] [,5034] [,5035] [,5036] [,5037] [,5038]
[1,] 0 0 0 0 0 0 1 1 1
[2,] 0 0 0 0 0 0 0 0 0
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[6,] 1 0 0 0 0 0 0 1 0
[7,] 0 0 0 1 0 0 1 1 0
[8,] 0 0 0 0 0 1 0 0 0
[9,] 0 0 0 0 1 0 0 0 0
[10,] 0 0 0 1 0 0 0 0 0
[,5039] [,5040] [,5041] [,5042] [,5043] [,5044] [,5045] [,5046] [,5047]
[1,] 1 0 0 1 0 0 0 0 1
[2,] 0 1 1 1 0 1 1 0 0
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[8,] 1 0 0 0 0 0 1 1 1
[9,] 1 0 1 0 0 0 0 1 1
[10,] 1 0 0 0 1 0 0 0 1
[,5048] [,5049] [,5050] [,5051] [,5052] [,5053] [,5054] [,5055] [,5056]
[1,] 1 0 1 0 0 0 0 1 1
[2,] 0 0 0 1 0 0 0 0 0
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[7,] 0 0 1 0 0 0 1 0 0
[8,] 0 0 1 1 1 1 0 0 1
[9,] 1 0 0 0 0 0 0 0 0
[10,] 0 0 0 0 0 0 1 1 0
[,5057] [,5058] [,5059] [,5060] [,5061] [,5062] [,5063] [,5064] [,5065]
[1,] 0 0 1 0 0 0 1 0 0
[2,] 0 0 0 0 1 0 0 0 0
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[7,] 1 1 0 0 0 0 0 1 0
[8,] 0 0 0 0 0 0 0 1 1
[9,] 0 0 0 0 0 0 0 0 1
[10,] 0 1 0 0 0 0 1 0 0
[,5066] [,5067] [,5068] [,5069] [,5070] [,5071] [,5072] [,5073] [,5074]
[1,] 0 0 1 1 0 1 0 0 0
[2,] 0 0 0 1 0 0 0 1 1
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[7,] 0 0 0 0 1 0 0 0 0
[8,] 0 1 0 1 0 1 0 0 1
[9,] 1 1 0 0 0 0 0 1 0
[10,] 0 1 0 0 0 1 0 1 0
[,5075] [,5076] [,5077] [,5078] [,5079] [,5080] [,5081] [,5082] [,5083]
[1,] 1 0 0 1 0 1 1 1 0
[2,] 0 0 0 0 0 1 0 0 0
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[7,] 0 0 1 0 0 1 0 0 0
[8,] 1 0 1 0 0 0 0 0 0
[9,] 0 0 0 1 0 1 0 0 0
[10,] 0 0 1 0 1 1 1 0 1
[,5084] [,5085] [,5086] [,5087] [,5088] [,5089] [,5090] [,5091] [,5092]
[1,] 0 0 0 0 0 0 0 1 1
[2,] 0 0 0 1 0 0 0 0 0
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[6,] 0 0 0 0 1 0 1 0 0
[7,] 1 0 0 0 0 0 0 1 0
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[9,] 0 0 0 0 1 0 0 0 0
[10,] 0 0 1 0 1 0 0 0 1
[,5093] [,5094] [,5095] [,5096] [,5097] [,5098] [,5099] [,5100] [,5101]
[1,] 0 0 1 0 0 0 0 0 0
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[10,] 0 0 1 0 0 0 0 0 0
[,5102] [,5103] [,5104] [,5105] [,5106] [,5107] [,5108] [,5109] [,5110]
[1,] 1 0 0 1 1 0 0 0 0
[2,] 1 0 0 0 0 0 0 0 0
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[8,] 0 0 0 0 0 0 0 0 0
[9,] 1 0 0 0 0 0 0 0 0
[10,] 0 0 1 0 1 0 1 1 0
[,5111] [,5112] [,5113] [,5114] [,5115] [,5116] [,5117] [,5118] [,5119]
[1,] 0 0 1 1 0 0 0 0 0
[2,] 0 0 0 1 1 1 0 1 1
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[7,] 1 1 0 0 1 1 0 0 0
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[10,] 1 0 0 0 1 0 1 0 0
[,5120] [,5121] [,5122] [,5123] [,5124] [,5125] [,5126] [,5127] [,5128]
[1,] 0 0 0 1 0 0 0 0 0
[2,] 0 1 0 0 0 0 0 1 1
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[8,] 1 0 1 1 1 0 1 0 0
[9,] 0 0 0 1 0 0 0 1 0
[10,] 1 0 1 0 0 0 0 0 0
[,5129] [,5130] [,5131] [,5132] [,5133] [,5134] [,5135] [,5136] [,5137]
[1,] 0 1 1 0 1 0 0 0 0
[2,] 0 0 0 1 0 0 0 0 1
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[10,] 0 0 1 0 0 1 0 0 0
[,5138] [,5139] [,5140] [,5141] [,5142] [,5143] [,5144] [,5145] [,5146]
[1,] 0 0 1 0 0 0 1 1 0
[2,] 0 0 1 0 0 0 1 0 0
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[8,] 0 0 0 0 0 0 0 0 1
[9,] 0 0 0 1 0 0 1 0 0
[10,] 1 0 0 0 0 0 0 1 0
[,5147] [,5148] [,5149] [,5150] [,5151] [,5152] [,5153] [,5154] [,5155]
[1,] 0 0 0 1 1 1 0 1 1
[2,] 0 0 1 0 1 0 0 0 0
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[10,] 0 1 1 0 0 0 0 0 0
[,5156] [,5157] [,5158] [,5159] [,5160] [,5161] [,5162] [,5163] [,5164]
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[,5165] [,5166] [,5167] [,5168] [,5169] [,5170] [,5171] [,5172] [,5173]
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[8,] 0 1 0 1 0 0 1 1 0
[9,] 0 1 1 0 0 1 0 1 0
[10,] 1 0 0 1 0 0 0 0 0
[,5174] [,5175] [,5176] [,5177] [,5178] [,5179] [,5180] [,5181] [,5182]
[1,] 1 1 0 0 0 0 0 0 0
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[10,] 0 0 1 0 0 1 0 0 0
[,5183] [,5184] [,5185] [,5186] [,5187] [,5188] [,5189] [,5190] [,5191]
[1,] 1 0 0 1 0 0 1 1 0
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[10,] 0 1 0 1 1 0 0 0 1
[,5192] [,5193] [,5194] [,5195] [,5196] [,5197] [,5198] [,5199] [,5200]
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[8,] 0 0 0 1 0 0 1 1 1
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[,5201] [,5202] [,5203] [,5204] [,5205] [,5206] [,5207] [,5208] [,5209]
[1,] 1 1 1 0 0 0 0 1 0
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[10,] 0 0 0 1 0 0 0 0 1
[,5210] [,5211] [,5212] [,5213] [,5214] [,5215] [,5216] [,5217] [,5218]
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[10,] 0 0 0 0 0 0 0 0 0
[,5219] [,5220] [,5221] [,5222] [,5223] [,5224] [,5225] [,5226] [,5227]
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[10,] 1 0 0 0 0 0 0 0 0
[,5228] [,5229] [,5230] [,5231] [,5232] [,5233] [,5234] [,5235] [,5236]
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[10,] 0 0 0 1 0 0 0 0 1
[,5237] [,5238] [,5239] [,5240] [,5241] [,5242] [,5243] [,5244] [,5245]
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[10,] 0 1 0 1 0 0 0 0 0
[,5246] [,5247] [,5248] [,5249] [,5250] [,5251] [,5252] [,5253] [,5254]
[1,] 1 0 0 0 0 1 0 0 0
[2,] 0 0 0 1 0 0 0 0 0
[3,] 0 0 1 1 0 0 0 0 0
[4,] 0 0 0 0 1 0 1 0 0
[5,] 0 0 0 1 1 1 0 0 0
[6,] 0 0 1 1 0 0 0 0 0
[7,] 0 0 0 0 0 1 0 0 0
[8,] 0 1 0 1 0 0 0 0 1
[9,] 1 0 0 0 1 0 1 0 0
[10,] 0 0 1 0 0 1 0 0 1
[,5255] [,5256] [,5257] [,5258] [,5259] [,5260] [,5261] [,5262] [,5263]
[1,] 1 1 0 0 0 0 1 0 1
[2,] 1 1 1 1 0 0 1 0 0
[3,] 0 0 0 1 0 0 1 0 0
[4,] 0 0 0 0 1 0 0 0 0
[5,] 1 0 1 0 1 1 0 1 0
[6,] 0 0 1 1 0 0 0 0 0
[7,] 1 0 1 0 0 0 1 1 1
[8,] 0 0 0 0 1 0 0 0 0
[9,] 0 0 0 0 1 1 0 0 0
[10,] 1 1 1 0 0 1 1 1 0
[,5264] [,5265] [,5266] [,5267] [,5268] [,5269] [,5270] [,5271] [,5272]
[1,] 0 1 0 1 1 0 0 0 0
[2,] 0 0 0 1 0 0 1 0 0
[3,] 1 0 0 0 0 1 0 1 0
[4,] 1 1 1 0 1 0 0 0 1
[5,] 0 0 1 0 1 0 0 0 1
[6,] 0 0 1 0 0 0 1 0 0
[7,] 0 0 1 0 0 1 0 1 0
[8,] 0 0 0 0 0 0 1 0 0
[9,] 1 0 0 0 0 0 0 0 0
[10,] 0 0 0 1 0 0 0 0 0
[,5273] [,5274] [,5275] [,5276] [,5277] [,5278] [,5279] [,5280] [,5281]
[1,] 0 1 1 0 0 0 0 0 0
[2,] 0 0 0 1 1 0 0 0 0
[3,] 0 1 0 1 1 0 0 0 0
[4,] 0 1 0 0 0 0 1 0 0
[5,] 1 1 0 0 1 0 1 0 0
[6,] 0 0 1 0 1 0 0 0 0
[7,] 0 0 0 0 1 1 0 0 0
[8,] 0 0 1 1 0 0 1 1 1
[9,] 1 0 1 0 0 0 0 0 0
[10,] 0 1 0 1 0 0 0 0 1
[,5282] [,5283] [,5284] [,5285] [,5286] [,5287] [,5288] [,5289] [,5290]
[1,] 0 0 0 0 1 0 0 0 0
[2,] 1 1 0 0 0 0 0 1 0
[3,] 1 0 1 0 1 0 0 0 1
[4,] 0 0 1 1 0 0 0 0 0
[5,] 0 0 1 1 0 1 0 0 0
[6,] 0 1 0 0 1 1 0 1 0
[7,] 0 0 1 0 0 0 0 0 0
[8,] 0 0 1 1 1 0 0 0 0
[9,] 0 0 0 0 0 0 1 0 0
[10,] 0 0 0 0 0 1 0 0 1
[,5291] [,5292] [,5293] [,5294] [,5295] [,5296] [,5297] [,5298] [,5299]
[1,] 0 0 0 0 1 0 1 0 0
[2,] 0 0 0 0 1 0 0 0 0
[3,] 0 0 0 0 0 1 0 0 1
[4,] 0 1 0 1 0 0 0 0 1
[5,] 1 0 0 0 1 0 0 0 0
[6,] 0 0 0 1 0 0 0 0 0
[7,] 0 1 0 0 0 0 0 1 1
[8,] 0 0 0 0 1 1 0 0 1
[9,] 0 1 1 1 1 1 0 0 0
[10,] 0 0 0 0 1 1 0 0 1
[,5300] [,5301] [,5302] [,5303] [,5304] [,5305] [,5306] [,5307] [,5308]
[1,] 0 0 1 0 0 1 1 0 0
[2,] 1 0 0 0 1 0 0 0 0
[3,] 1 0 1 0 1 0 0 0 1
[4,] 0 1 0 1 1 1 0 0 1
[5,] 0 0 0 0 1 0 0 0 0
[6,] 0 0 0 1 1 0 0 0 0
[7,] 0 0 0 0 0 1 0 1 1
[8,] 0 0 0 0 0 1 1 0 0
[9,] 0 0 0 1 1 1 1 1 0
[10,] 0 1 1 0 0 0 0 0 1
[,5309] [,5310] [,5311] [,5312] [,5313] [,5314] [,5315] [,5316] [,5317]
[1,] 0 0 0 0 0 0 1 1 1
[2,] 0 0 1 0 0 0 0 0 0
[3,] 0 0 1 0 0 0 1 0 1
[4,] 0 0 0 0 1 0 0 0 0
[5,] 0 0 0 0 1 1 1 0 0
[6,] 0 0 1 0 0 0 0 0 1
[7,] 0 0 1 0 0 0 0 0 0
[8,] 1 0 0 1 0 0 0 0 0
[9,] 0 0 0 1 0 0 0 0 1
[10,] 0 0 0 0 0 0 0 0 0
[,5318] [,5319] [,5320] [,5321] [,5322] [,5323] [,5324] [,5325] [,5326]
[1,] 0 0 0 1 0 0 0 0 0
[2,] 0 1 1 1 0 0 0 0 0
[3,] 0 0 0 0 1 1 0 0 0
[4,] 0 0 0 0 0 0 0 0 0
[5,] 1 0 1 0 0 0 0 0 0
[6,] 1 1 1 0 0 0 0 0 0
[7,] 0 0 0 0 0 0 0 0 0
[8,] 1 0 0 0 0 0 0 0 0
[9,] 0 0 1 0 1 0 0 0 0
[10,] 1 1 0 0 1 0 0 0 0
[,5327] [,5328] [,5329] [,5330] [,5331] [,5332] [,5333] [,5334] [,5335]
[1,] 0 1 0 1 0 0 0 1 0
[2,] 0 1 0 1 0 0 1 0 1
[3,] 0 0 0 0 0 1 0 0 0
[4,] 1 1 0 0 0 0 0 0 0
[5,] 1 0 0 0 0 1 0 1 1
[6,] 1 1 0 0 0 0 0 1 0
[7,] 1 0 0 0 0 0 0 0 0
[8,] 0 1 0 1 0 0 0 0 1
[9,] 1 0 0 0 0 1 0 0 0
[10,] 0 0 0 0 0 1 0 1 0
[,5336] [,5337] [,5338] [,5339] [,5340] [,5341] [,5342] [,5343] [,5344]
[1,] 0 0 0 0 0 0 1 1 0
[2,] 1 0 1 1 1 0 1 1 0
[3,] 0 0 0 1 0 0 1 0 0
[4,] 0 0 1 0 0 0 1 0 0
[5,] 0 0 0 0 0 0 0 0 0
[6,] 0 0 1 0 1 0 0 1 0
[7,] 0 0 0 1 0 0 1 1 1
[8,] 0 1 0 0 1 0 1 0 1
[9,] 0 0 0 0 0 0 0 0 0
[10,] 0 0 1 1 0 0 1 0 0
[,5345] [,5346] [,5347] [,5348] [,5349] [,5350] [,5351] [,5352] [,5353]
[1,] 1 0 0 0 0 0 1 0 0
[2,] 0 0 0 0 1 0 1 0 0
[3,] 1 0 0 1 0 0 0 0 0
[4,] 1 1 0 0 0 1 0 0 1
[5,] 1 0 0 0 0 0 0 0 1
[6,] 0 0 0 1 1 1 0 1 0
[7,] 0 0 0 1 1 0 1 0 0
[8,] 0 0 1 0 0 1 0 0 1
[9,] 1 0 1 1 1 0 0 0 0
[10,] 0 1 1 1 0 0 0 0 1
[,5354] [,5355] [,5356] [,5357] [,5358] [,5359] [,5360] [,5361] [,5362]
[1,] 1 1 1 0 1 0 0 1 1
[2,] 0 0 1 0 0 0 0 0 0
[3,] 1 0 0 1 0 1 0 0 0
[4,] 0 0 0 0 1 0 0 0 0
[5,] 0 0 0 0 0 0 1 0 0
[6,] 1 0 0 0 1 1 1 0 0
[7,] 0 0 0 0 0 0 0 0 1
[8,] 1 0 0 0 0 1 1 0 0
[9,] 0 0 1 0 1 1 1 1 0
[10,] 0 0 0 0 0 0 1 1 0
[,5363] [,5364] [,5365] [,5366] [,5367] [,5368] [,5369] [,5370] [,5371]
[1,] 0 0 0 0 0 0 0 0 1
[2,] 0 0 1 0 0 1 0 0 0
[3,] 0 0 1 1 0 0 0 0 1
[4,] 0 0 1 0 0 0 0 0 0
[5,] 0 1 1 1 0 0 0 0 0
[6,] 0 0 0 0 0 1 1 1 0
[7,] 0 1 1 0 1 1 0 0 0
[8,] 0 0 0 0 0 0 0 1 1
[9,] 0 0 0 0 0 1 0 0 0
[10,] 0 0 1 1 0 0 0 0 0
[,5372] [,5373] [,5374] [,5375] [,5376] [,5377] [,5378] [,5379] [,5380]
[1,] 0 0 0 0 1 1 0 0 1
[2,] 0 0 0 1 0 0 0 1 0
[3,] 0 0 1 0 1 0 0 1 0
[4,] 0 1 0 0 0 0 0 0 1
[5,] 0 0 0 0 0 0 1 0 1
[6,] 0 0 0 0 0 1 0 0 1
[7,] 0 0 0 0 1 1 0 1 1
[8,] 1 1 0 0 1 0 0 1 0
[9,] 0 0 1 0 0 0 1 1 0
[10,] 1 1 1 0 0 1 1 0 0
[,5381] [,5382] [,5383] [,5384] [,5385] [,5386] [,5387] [,5388] [,5389]
[1,] 0 1 0 0 0 0 1 0 0
[2,] 0 0 1 0 0 0 0 1 0
[3,] 1 0 1 1 0 0 1 1 0
[4,] 0 0 1 1 1 0 1 1 0
[5,] 0 1 0 0 0 0 0 0 0
[6,] 0 0 0 0 0 0 0 0 0
[7,] 0 1 1 0 1 0 0 0 0
[8,] 0 0 0 1 0 0 1 0 0
[9,] 0 0 0 0 1 0 0 1 0
[10,] 0 0 0 1 0 0 0 0 0
[,5390] [,5391] [,5392] [,5393] [,5394] [,5395] [,5396] [,5397] [,5398]
[1,] 0 0 0 0 1 0 1 0 1
[2,] 0 1 0 0 0 0 0 1 0
[3,] 1 0 1 0 0 0 0 1 0
[4,] 1 0 0 1 0 0 0 0 0
[5,] 0 0 0 0 0 0 0 0 0
[6,] 1 1 0 1 0 0 0 0 1
[7,] 0 0 0 0 0 0 0 0 1
[8,] 0 0 0 0 1 0 0 0 0
[9,] 1 0 0 1 0 1 0 0 0
[10,] 1 1 1 0 0 0 1 1 0
[,5399] [,5400] [,5401] [,5402] [,5403] [,5404] [,5405] [,5406] [,5407]
[1,] 0 0 0 0 0 0 1 1 1
[2,] 0 1 0 0 1 0 1 0 0
[3,] 0 0 0 1 0 1 0 0 0
[4,] 1 0 0 0 0 0 0 0 0
[5,] 0 1 1 0 0 0 0 1 0
[6,] 0 0 0 0 1 0 0 0 1
[7,] 0 1 0 0 1 1 0 0 0
[8,] 0 1 0 0 0 1 1 0 0
[9,] 1 0 0 0 1 1 0 0 1
[10,] 0 0 0 1 0 1 0 0 0
[,5408] [,5409] [,5410] [,5411] [,5412] [,5413] [,5414] [,5415] [,5416]
[1,] 0 0 0 0 0 1 0 1 0
[2,] 0 1 1 0 1 1 0 0 0
[3,] 0 0 1 1 0 1 0 0 1
[4,] 0 0 0 0 0 1 0 1 0
[5,] 0 0 0 0 0 0 1 0 0
[6,] 0 0 0 0 0 0 0 1 1
[7,] 0 0 0 0 0 1 0 0 0
[8,] 0 0 0 0 0 0 0 0 0
[9,] 0 1 0 0 0 0 1 0 0
[10,] 0 0 0 0 0 0 1 0 1
[,5417] [,5418] [,5419] [,5420] [,5421] [,5422] [,5423] [,5424] [,5425]
[1,] 1 0 0 0 0 0 0 0 0
[2,] 0 0 0 0 1 1 0 0 1
[3,] 0 0 0 0 0 0 1 0 0
[4,] 0 0 0 0 0 0 0 0 0
[5,] 0 0 0 0 0 0 1 1 0
[6,] 0 0 0 0 0 0 0 1 0
[7,] 0 1 0 0 1 0 0 0 1
[8,] 0 0 0 0 0 0 0 0 0
[9,] 1 0 0 1 0 0 0 0 1
[10,] 1 1 0 0 1 0 0 0 0
[,5426] [,5427] [,5428] [,5429] [,5430] [,5431] [,5432] [,5433] [,5434]
[1,] 1 0 0 0 1 0 0 0 1
[2,] 0 0 0 0 0 0 0 0 0
[3,] 0 0 0 0 0 0 0 0 0
[4,] 0 1 0 0 1 1 0 0 1
[5,] 0 1 0 0 0 0 1 0 0
[6,] 0 1 1 0 0 1 0 1 1
[7,] 0 0 0 0 0 0 0 0 0
[8,] 0 0 1 0 1 1 0 0 0
[9,] 0 1 1 0 0 1 0 0 0
[10,] 0 0 0 0 1 0 0 0 1
[,5435] [,5436] [,5437] [,5438] [,5439] [,5440] [,5441] [,5442] [,5443]
[1,] 0 0 0 0 0 1 0 1 0
[2,] 1 1 1 0 1 1 0 0 0
[3,] 0 1 0 1 1 0 1 0 0
[4,] 0 1 0 0 0 0 0 0 0
[5,] 1 1 1 0 0 0 1 0 0
[6,] 0 0 1 0 0 0 1 0 0
[7,] 0 0 0 0 0 0 0 0 0
[8,] 0 1 0 0 0 0 0 0 0
[9,] 0 0 0 0 0 0 0 0 1
[10,] 0 1 0 0 1 0 0 0 0
[,5444] [,5445] [,5446] [,5447] [,5448] [,5449] [,5450] [,5451] [,5452]
[1,] 1 0 1 1 0 1 0 1 0
[2,] 0 1 0 0 1 0 0 0 0
[3,] 0 0 0 0 1 0 0 0 1
[4,] 0 1 0 0 1 1 0 0 0
[5,] 1 1 0 0 0 0 0 1 0
[6,] 0 0 0 0 0 0 0 0 0
[7,] 0 1 0 0 0 0 0 0 0
[8,] 0 0 0 0 0 0 0 0 0
[9,] 0 0 1 0 1 0 0 0 0
[10,] 0 0 1 0 0 0 0 0 1
[,5453] [,5454] [,5455] [,5456] [,5457] [,5458] [,5459] [,5460] [,5461]
[1,] 1 0 0 1 0 0 1 0 0
[2,] 0 0 0 0 0 0 1 1 0
[3,] 0 0 1 1 1 0 0 1 0
[4,] 0 0 1 0 1 0 0 0 0
[5,] 0 0 1 1 0 0 0 0 0
[6,] 0 0 0 1 0 0 0 0 0
[7,] 1 0 1 1 0 0 1 0 0
[8,] 0 0 0 1 0 0 0 0 0
[9,] 0 0 0 0 0 0 0 0 0
[10,] 0 1 1 0 0 0 1 0 0
[,5462] [,5463] [,5464] [,5465] [,5466] [,5467] [,5468] [,5469] [,5470]
[1,] 0 0 0 1 0 0 1 0 0
[2,] 1 0 0 0 0 0 0 0 1
[3,] 0 0 0 0 0 1 1 0 0
[4,] 0 0 0 0 0 0 0 0 0
[5,] 1 0 0 0 0 1 0 0 1
[6,] 0 0 0 0 0 0 0 0 0
[7,] 0 1 0 0 0 0 0 0 0
[8,] 1 0 1 1 0 1 1 0 0
[9,] 0 0 0 0 0 0 1 0 0
[10,] 1 0 1 1 0 0 0 0 0
[,5471] [,5472] [,5473] [,5474] [,5475] [,5476] [,5477] [,5478] [,5479]
[1,] 1 0 1 1 0 0 1 1 0
[2,] 1 0 0 0 1 0 0 0 0
[3,] 0 0 0 0 1 0 1 0 0
[4,] 0 1 0 0 0 1 0 0 0
[5,] 0 0 0 0 0 0 1 0 0
[6,] 0 0 0 0 1 0 1 1 1
[7,] 1 0 0 0 0 0 0 0 0
[8,] 1 0 0 0 0 0 1 0 0
[9,] 0 0 0 1 0 1 0 0 0
[10,] 1 0 0 1 0 0 0 0 0
[,5480] [,5481] [,5482] [,5483] [,5484] [,5485] [,5486] [,5487] [,5488]
[1,] 0 1 0 0 0 1 1 0 0
[2,] 1 1 0 0 1 1 0 0 0
[3,] 1 0 0 0 0 0 0 1 0
[4,] 0 0 1 0 0 0 0 1 1
[5,] 1 0 0 1 0 0 0 1 0
[6,] 0 0 0 1 0 1 0 0 0
[7,] 1 1 0 0 0 0 0 1 1
[8,] 0 0 0 1 0 1 0 0 0
[9,] 0 1 0 0 0 1 0 0 0
[10,] 1 0 0 0 0 0 0 1 0
[,5489] [,5490] [,5491] [,5492] [,5493] [,5494] [,5495] [,5496] [,5497]
[1,] 0 0 0 0 0 0 0 0 0
[2,] 0 0 0 0 0 0 0 0 0
[3,] 0 0 0 0 0 1 0 0 1
[4,] 0 0 0 0 0 0 0 0 0
[5,] 1 0 0 1 1 1 0 0 1
[6,] 1 1 0 0 1 0 0 1 1
[7,] 0 0 0 1 0 0 0 1 0
[8,] 0 0 1 0 0 1 0 1 1
[9,] 1 1 0 1 0 0 0 0 1
[10,] 0 0 0 1 0 1 1 0 0
[,5498] [,5499] [,5500] [,5501] [,5502] [,5503] [,5504] [,5505] [,5506]
[1,] 1 0 0 1 0 0 1 0 0
[2,] 0 0 0 0 0 0 0 0 0
[3,] 0 0 0 0 0 0 0 0 0
[4,] 0 0 0 0 1 0 0 1 0
[5,] 1 0 0 0 0 0 0 0 0
[6,] 0 1 0 0 0 0 0 0 0
[7,] 0 0 0 0 0 0 0 0 0
[8,] 1 1 0 0 0 0 0 1 0
[9,] 1 1 0 1 0 1 1 0 1
[10,] 0 0 0 0 0 1 0 0 0
[,5507] [,5508] [,5509] [,5510] [,5511] [,5512] [,5513] [,5514] [,5515]
[1,] 0 0 0 1 1 0 0 0 0
[2,] 0 1 1 0 0 1 1 0 1
[3,] 1 0 0 0 1 0 0 0 0
[4,] 0 0 0 0 0 0 0 1 0
[5,] 0 1 1 0 0 0 1 0 0
[6,] 0 0 0 0 0 1 1 1 1
[7,] 1 0 0 0 1 0 0 0 0
[8,] 0 0 0 0 0 0 1 0 0
[9,] 0 0 0 1 1 1 0 0 1
[10,] 0 0 0 0 1 0 0 0 0
[,5516] [,5517] [,5518] [,5519] [,5520] [,5521] [,5522] [,5523] [,5524]
[1,] 0 1 0 0 0 0 0 0 0
[2,] 0 0 0 0 0 0 1 1 0
[3,] 0 1 1 1 0 0 0 0 0
[4,] 1 1 1 0 0 0 0 0 0
[5,] 1 0 0 0 1 0 0 0 0
[6,] 0 1 0 1 0 0 1 0 0
[7,] 0 0 0 0 1 1 0 0 0
[8,] 0 0 1 0 0 1 1 0 0
[9,] 0 0 0 0 0 1 1 0 0
[10,] 1 1 0 0 0 0 1 0 0
[,5525] [,5526] [,5527] [,5528] [,5529] [,5530] [,5531] [,5532] [,5533]
[1,] 1 0 1 1 0 1 0 0 0
[2,] 0 0 0 0 1 0 1 0 0
[3,] 1 0 0 1 0 0 0 1 0
[4,] 0 0 1 0 0 0 0 0 0
[5,] 1 1 0 1 0 0 0 0 0
[6,] 0 0 0 0 1 0 0 0 0
[7,] 0 1 0 0 1 0 0 1 1
[8,] 0 0 1 0 1 0 1 0 0
[9,] 0 0 0 0 0 0 0 0 0
[10,] 0 0 0 1 0 0 1 1 0
[,5534] [,5535] [,5536] [,5537] [,5538] [,5539] [,5540] [,5541] [,5542]
[1,] 0 0 0 0 0 1 0 0 0
[2,] 0 0 0 0 1 0 1 0 0
[3,] 0 1 1 0 1 1 0 1 0
[4,] 1 0 0 0 0 0 0 0 0
[5,] 0 0 1 0 0 0 0 0 0
[6,] 1 0 0 0 1 1 0 0 1
[7,] 0 0 0 0 0 0 1 1 1
[8,] 0 0 0 0 1 0 0 0 1
[9,] 0 0 0 0 0 0 0 0 0
[10,] 0 1 1 0 0 0 0 0 1
[,5543] [,5544] [,5545] [,5546] [,5547] [,5548] [,5549] [,5550] [,5551]
[1,] 0 0 0 1 0 0 0 1 0
[2,] 0 1 1 1 0 0 1 0 0
[3,] 0 0 0 1 0 1 0 1 0
[4,] 0 1 0 0 0 1 0 0 0
[5,] 0 1 0 0 0 0 1 1 0
[6,] 1 0 1 0 1 0 0 1 0
[7,] 0 1 0 0 0 0 1 0 0
[8,] 0 0 0 0 0 0 0 1 1
[9,] 0 0 0 0 0 0 1 0 0
[10,] 0 1 1 0 0 0 0 0 0
[,5552] [,5553] [,5554] [,5555] [,5556] [,5557] [,5558] [,5559] [,5560]
[1,] 0 0 0 0 0 1 0 1 1
[2,] 0 1 1 0 0 1 1 0 0
[3,] 1 0 0 0 0 1 1 1 1
[4,] 0 1 1 0 0 0 0 0 0
[5,] 0 0 0 0 0 1 1 0 0
[6,] 0 0 0 0 0 0 1 0 0
[7,] 0 1 1 0 0 0 0 0 0
[8,] 0 1 1 0 0 0 0 0 0
[9,] 0 0 0 0 0 0 1 0 0
[10,] 0 0 0 1 0 0 0 0 0
[,5561] [,5562] [,5563] [,5564] [,5565] [,5566] [,5567] [,5568] [,5569]
[1,] 0 0 0 0 1 0 1 1 1
[2,] 1 0 0 0 0 0 0 0 0
[3,] 0 0 0 0 0 1 0 0 0
[4,] 1 0 0 1 0 0 1 0 0
[5,] 1 0 0 0 0 0 0 0 0
[6,] 0 0 0 0 0 0 1 0 0
[7,] 0 0 0 0 0 0 1 0 0
[8,] 0 0 0 0 0 0 0 0 1
[9,] 1 0 0 1 1 0 1 0 0
[10,] 0 0 0 1 1 1 0 0 0
[,5570] [,5571] [,5572] [,5573] [,5574] [,5575] [,5576] [,5577] [,5578]
[1,] 0 0 0 0 0 1 0 1 0
[2,] 0 0 0 0 0 0 0 1 1
[3,] 0 0 1 1 0 0 1 0 0
[4,] 0 1 0 0 0 1 0 0 0
[5,] 0 1 0 1 1 0 0 0 1
[6,] 0 1 1 0 0 0 0 0 0
[7,] 0 0 0 0 0 1 1 0 0
[8,] 0 0 0 1 0 1 1 0 0
[9,] 0 0 0 0 0 0 1 0 0
[10,] 0 0 0 0 0 1 1 1 0
[,5579] [,5580] [,5581] [,5582] [,5583] [,5584] [,5585] [,5586] [,5587]
[1,] 0 1 1 1 0 1 0 0 1
[2,] 0 0 0 1 0 0 1 0 0
[3,] 0 0 0 1 0 0 1 0 0
[4,] 0 0 0 0 0 0 0 0 0
[5,] 0 0 1 0 0 0 0 0 1
[6,] 0 0 0 0 0 1 0 0 0
[7,] 0 0 0 0 0 1 0 1 0
[8,] 1 1 1 0 0 0 0 0 0
[9,] 0 0 0 0 1 1 0 0 0
[10,] 0 0 1 1 1 0 0 1 0
[,5588] [,5589] [,5590] [,5591] [,5592] [,5593] [,5594] [,5595] [,5596]
[1,] 0 1 0 0 0 0 1 0 0
[2,] 0 0 1 1 0 0 1 0 0
[3,] 1 0 0 0 0 0 1 0 0
[4,] 0 0 0 0 0 0 1 1 0
[5,] 0 0 1 1 0 0 1 0 0
[6,] 0 0 0 0 1 0 0 0 0
[7,] 0 0 1 0 1 0 0 0 1
[8,] 0 0 0 0 1 0 0 0 0
[9,] 0 0 0 0 1 0 0 0 0
[10,] 1 1 0 1 0 0 0 0 0
[,5597] [,5598] [,5599] [,5600] [,5601] [,5602] [,5603] [,5604] [,5605]
[1,] 0 0 0 1 0 0 0 0 1
[2,] 0 0 0 0 0 0 0 0 0
[3,] 1 1 0 0 0 0 1 1 0
[4,] 0 0 1 1 1 0 1 0 1
[5,] 0 0 0 0 0 0 1 1 1
[6,] 0 0 0 0 0 0 1 0 0
[7,] 0 0 0 0 1 1 1 0 1
[8,] 0 0 0 1 0 0 0 0 0
[9,] 1 1 0 0 0 1 0 0 1
[10,] 0 1 1 0 0 0 0 1 0
[,5606] [,5607] [,5608] [,5609] [,5610] [,5611] [,5612] [,5613] [,5614]
[1,] 0 0 0 0 0 1 0 0 0
[2,] 0 0 0 1 0 0 0 0 0
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[5,] 0 0 0 1 0 0 0 1 0
[6,] 0 0 0 0 1 0 0 0 0
[7,] 0 0 0 0 0 0 0 0 0
[8,] 0 1 0 0 0 1 0 0 0
[9,] 0 0 1 0 0 0 0 0 0
[10,] 1 0 0 0 1 0 0 0 0
[,5615] [,5616] [,5617] [,5618] [,5619] [,5620] [,5621] [,5622] [,5623]
[1,] 0 1 0 0 0 0 0 0 0
[2,] 0 1 0 1 1 1 0 1 0
[3,] 0 0 0 1 1 0 0 1 0
[4,] 0 0 0 0 1 0 0 1 0
[5,] 0 0 0 0 0 1 1 1 0
[6,] 1 1 0 0 0 0 0 0 0
[7,] 0 0 0 0 0 1 0 1 0
[8,] 0 0 0 0 0 0 0 1 0
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[10,] 0 0 0 0 0 0 0 0 0
[,5624] [,5625] [,5626] [,5627] [,5628] [,5629] [,5630] [,5631] [,5632]
[1,] 0 0 1 0 0 0 1 1 0
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[6,] 0 1 0 0 0 0 0 0 1
[7,] 0 0 0 0 1 0 0 1 0
[8,] 0 0 1 0 0 0 0 0 1
[9,] 0 1 0 0 0 0 0 0 0
[10,] 1 0 1 1 1 1 1 0 1
[,5633] [,5634] [,5635] [,5636] [,5637] [,5638] [,5639] [,5640] [,5641]
[1,] 0 0 1 0 1 0 1 1 0
[2,] 1 1 0 0 0 1 0 0 1
[3,] 1 0 1 0 0 0 1 1 0
[4,] 0 1 0 1 1 0 0 0 0
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[6,] 0 0 0 0 1 0 0 0 1
[7,] 0 1 0 0 0 0 0 0 0
[8,] 0 1 0 0 0 0 1 0 0
[9,] 0 1 1 0 1 0 0 1 1
[10,] 0 0 0 1 1 0 0 0 0
[,5642] [,5643] [,5644] [,5645] [,5646] [,5647] [,5648] [,5649] [,5650]
[1,] 1 1 0 0 0 0 1 0 0
[2,] 1 1 0 0 1 0 1 0 1
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[4,] 0 1 0 0 0 0 0 1 0
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[6,] 0 1 0 0 1 0 0 0 0
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[8,] 0 1 0 0 0 0 0 0 1
[9,] 0 0 0 0 0 1 0 1 0
[10,] 1 0 0 0 1 0 1 1 1
[,5651] [,5652] [,5653] [,5654] [,5655] [,5656] [,5657] [,5658] [,5659]
[1,] 1 0 0 0 0 1 0 0 1
[2,] 0 1 0 0 0 0 0 0 0
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[6,] 1 0 0 0 1 1 1 1 1
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[8,] 0 0 1 1 1 0 0 0 0
[9,] 1 0 0 0 1 0 0 1 0
[10,] 0 0 0 1 0 0 1 0 0
[,5660] [,5661] [,5662] [,5663] [,5664] [,5665] [,5666] [,5667] [,5668]
[1,] 1 1 0 1 0 0 0 0 0
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[8,] 0 1 0 1 0 0 0 0 0
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[,5669] [,5670] [,5671] [,5672] [,5673] [,5674] [,5675] [,5676] [,5677]
[1,] 0 0 1 0 0 0 0 0 0
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[,5678] [,5679] [,5680] [,5681] [,5682] [,5683] [,5684] [,5685] [,5686]
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[,5687] [,5688] [,5689] [,5690] [,5691] [,5692] [,5693] [,5694] [,5695]
[1,] 0 0 1 1 1 1 0 0 0
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[,5696] [,5697] [,5698] [,5699] [,5700] [,5701] [,5702] [,5703] [,5704]
[1,] 1 0 0 0 0 1 0 0 1
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[10,] 0 1 0 0 0 0 0 1 1
[,5705] [,5706] [,5707] [,5708] [,5709] [,5710] [,5711] [,5712] [,5713]
[1,] 0 0 0 0 0 1 1 0 0
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[8,] 1 0 0 0 1 0 0 0 1
[9,] 0 0 0 0 0 0 0 1 0
[10,] 0 0 0 0 0 1 0 1 0
[,5714] [,5715] [,5716] [,5717] [,5718] [,5719] [,5720] [,5721] [,5722]
[1,] 0 1 0 1 0 0 0 0 0
[2,] 1 1 1 0 0 1 0 0 0
[3,] 0 1 0 0 0 0 0 1 1
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[5,] 1 0 1 0 0 0 0 1 0
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[7,] 0 0 1 0 0 1 0 0 0
[8,] 1 0 0 1 1 0 1 0 1
[9,] 0 1 0 0 0 0 0 0 0
[10,] 0 0 1 0 0 1 0 1 0
[,5723] [,5724] [,5725] [,5726] [,5727] [,5728] [,5729] [,5730] [,5731]
[1,] 0 1 0 0 0 1 1 0 0
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[5,] 0 1 0 0 0 0 0 1 0
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[7,] 0 0 0 1 1 0 0 1 0
[8,] 0 0 0 1 1 0 1 0 0
[9,] 0 0 0 0 0 0 0 1 0
[10,] 1 1 0 1 0 1 1 0 1
[,5732] [,5733] [,5734] [,5735] [,5736] [,5737] [,5738] [,5739] [,5740]
[1,] 0 1 1 0 1 1 0 0 1
[2,] 0 1 1 0 0 0 1 0 0
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[7,] 0 0 0 1 0 0 0 0 0
[8,] 1 1 0 1 0 0 0 0 0
[9,] 0 1 0 1 0 0 1 0 0
[10,] 0 0 0 1 0 0 0 1 1
[,5741] [,5742] [,5743] [,5744] [,5745] [,5746] [,5747] [,5748] [,5749]
[1,] 0 0 0 0 0 0 0 0 1
[2,] 0 0 1 0 0 1 0 0 0
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[6,] 0 0 1 0 1 0 0 1 0
[7,] 0 0 0 0 0 0 0 0 0
[8,] 0 0 1 0 1 1 0 1 1
[9,] 0 0 0 0 0 0 0 0 0
[10,] 0 0 1 0 0 0 0 0 0
[,5750] [,5751] [,5752] [,5753] [,5754] [,5755] [,5756] [,5757] [,5758]
[1,] 0 1 0 0 1 0 1 0 1
[2,] 0 0 0 0 1 0 0 0 1
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[6,] 0 0 1 1 0 0 1 0 1
[7,] 0 0 1 0 0 0 1 0 0
[8,] 0 0 0 1 1 1 0 1 1
[9,] 0 0 1 0 0 0 0 0 0
[10,] 0 0 1 0 0 0 0 0 0
[,5759] [,5760] [,5761] [,5762] [,5763] [,5764] [,5765] [,5766] [,5767]
[1,] 0 0 0 0 1 0 1 0 1
[2,] 0 1 0 0 0 0 0 0 0
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[6,] 1 0 0 0 1 0 0 0 1
[7,] 0 0 0 0 1 0 0 0 0
[8,] 0 0 0 0 1 0 1 1 1
[9,] 0 1 0 0 0 1 0 1 1
[10,] 1 1 0 0 1 1 0 0 0
[,5768] [,5769] [,5770] [,5771] [,5772] [,5773] [,5774] [,5775] [,5776]
[1,] 0 1 0 1 0 1 0 0 0
[2,] 0 0 0 1 0 0 0 1 0
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[8,] 1 0 0 0 1 0 0 1 1
[9,] 0 1 0 0 0 0 1 0 0
[10,] 0 0 0 0 0 0 0 0 0
[,5777] [,5778] [,5779] [,5780] [,5781] [,5782] [,5783] [,5784] [,5785]
[1,] 0 0 1 0 0 0 1 1 0
[2,] 0 0 0 1 0 0 1 0 0
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[7,] 0 1 0 1 0 1 0 0 0
[8,] 0 0 0 0 0 1 0 1 0
[9,] 0 0 0 0 0 1 1 1 0
[10,] 1 0 1 0 1 0 1 1 0
[,5786] [,5787] [,5788] [,5789] [,5790] [,5791] [,5792] [,5793] [,5794]
[1,] 1 0 0 1 0 1 0 1 0
[2,] 0 0 1 0 0 1 0 1 1
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[7,] 1 1 1 0 0 1 0 0 0
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[9,] 0 1 0 1 0 0 0 0 0
[10,] 0 1 0 0 0 0 0 0 0
[,5795] [,5796] [,5797] [,5798] [,5799] [,5800] [,5801] [,5802] [,5803]
[1,] 0 0 0 0 1 1 1 0 0
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[8,] 1 1 0 0 1 0 0 0 1
[9,] 0 0 1 0 0 0 1 0 0
[10,] 1 0 0 0 1 0 0 1 1
[,5804] [,5805] [,5806] [,5807] [,5808] [,5809] [,5810] [,5811] [,5812]
[1,] 1 0 0 0 0 1 0 1 0
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[7,] 0 1 1 0 0 0 1 1 1
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[9,] 0 0 0 0 0 0 0 0 0
[10,] 0 0 0 0 1 0 0 1 0
[,5813] [,5814] [,5815] [,5816] [,5817] [,5818] [,5819] [,5820] [,5821]
[1,] 0 0 0 0 1 0 1 1 0
[2,] 0 1 0 1 0 1 0 1 1
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[7,] 0 0 1 1 0 0 0 0 0
[8,] 1 0 0 0 1 0 0 0 0
[9,] 0 0 1 0 0 0 0 0 0
[10,] 0 0 0 0 0 1 0 0 0
[,5822] [,5823] [,5824] [,5825] [,5826] [,5827] [,5828] [,5829] [,5830]
[1,] 0 1 1 0 0 0 0 0 1
[2,] 1 0 0 0 0 0 0 0 0
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[4,] 0 1 1 0 1 0 1 0 0
[5,] 1 1 0 0 0 0 0 1 1
[6,] 0 0 0 0 0 1 1 0 0
[7,] 0 0 0 1 0 0 0 0 0
[8,] 0 0 1 0 0 0 0 0 0
[9,] 0 0 0 1 0 1 0 0 0
[10,] 0 1 0 0 0 0 1 0 0
[,5831] [,5832] [,5833] [,5834] [,5835] [,5836] [,5837] [,5838] [,5839]
[1,] 0 0 0 0 0 0 0 0 0
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[7,] 1 0 1 0 1 1 0 0 1
[8,] 0 0 0 0 1 0 0 1 1
[9,] 0 0 0 1 0 0 0 1 0
[10,] 0 0 1 0 0 0 0 1 1
[,5840] [,5841] [,5842] [,5843] [,5844] [,5845] [,5846] [,5847] [,5848]
[1,] 0 0 1 1 1 0 1 1 1
[2,] 0 1 0 0 0 0 0 0 0
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[9,] 0 0 0 0 0 0 1 0 0
[10,] 0 0 0 0 0 1 1 0 0
[,5849] [,5850] [,5851] [,5852] [,5853] [,5854] [,5855] [,5856] [,5857]
[1,] 1 0 0 1 0 0 0 0 0
[2,] 0 0 0 0 1 0 0 1 0
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[9,] 0 0 0 0 1 0 0 0 0
[10,] 0 0 0 0 0 0 0 1 0
[,5858] [,5859] [,5860] [,5861] [,5862] [,5863] [,5864] [,5865] [,5866]
[1,] 0 0 0 0 0 0 1 0 1
[2,] 0 1 0 0 0 0 0 0 0
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[8,] 1 0 0 0 0 0 1 0 0
[9,] 0 1 0 0 1 1 1 0 1
[10,] 0 1 1 1 0 0 0 0 0
[,5867] [,5868] [,5869] [,5870] [,5871] [,5872] [,5873] [,5874] [,5875]
[1,] 0 0 0 1 1 0 0 1 1
[2,] 0 0 0 1 1 1 0 1 1
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[10,] 0 1 0 1 0 0 1 0 1
[,5876] [,5877] [,5878] [,5879] [,5880] [,5881] [,5882] [,5883] [,5884]
[1,] 1 0 0 1 0 0 1 0 0
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[10,] 0 0 1 0 0 1 0 1 1
[,5885] [,5886] [,5887] [,5888] [,5889] [,5890] [,5891] [,5892] [,5893]
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[,5894] [,5895] [,5896] [,5897] [,5898] [,5899] [,5900] [,5901] [,5902]
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[10,] 1 0 0 0 0 0 0 0 1
[,5903] [,5904] [,5905] [,5906] [,5907] [,5908] [,5909] [,5910] [,5911]
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[10,] 0 1 0 1 1 0 0 1 0
[,5912] [,5913] [,5914] [,5915] [,5916] [,5917] [,5918] [,5919] [,5920]
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[8,] 0 0 1 0 0 0 0 0 0
[9,] 0 1 0 1 1 1 1 0 1
[10,] 0 1 0 0 1 1 0 0 0
[,5921] [,5922] [,5923] [,5924] [,5925] [,5926] [,5927] [,5928] [,5929]
[1,] 0 0 1 0 0 0 0 0 0
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[9,] 1 0 0 0 0 0 0 1 1
[10,] 0 0 1 0 0 0 0 1 1
[,5930] [,5931] [,5932] [,5933] [,5934] [,5935] [,5936] [,5937] [,5938]
[1,] 0 1 0 1 0 0 1 0 1
[2,] 0 1 1 1 1 1 0 0 1
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[10,] 1 1 0 0 0 1 1 0 1
[,5939] [,5940] [,5941] [,5942] [,5943] [,5944] [,5945] [,5946] [,5947]
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[10,] 0 0 0 0 1 0 0 0 1
[,5948] [,5949] [,5950] [,5951] [,5952] [,5953] [,5954] [,5955] [,5956]
[1,] 1 0 0 1 0 1 0 0 0
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[10,] 1 0 1 0 1 0 1 0 0
[,5957] [,5958] [,5959] [,5960] [,5961] [,5962] [,5963] [,5964] [,5965]
[1,] 1 1 0 0 0 1 0 1 1
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[,5966] [,5967] [,5968] [,5969] [,5970] [,5971] [,5972] [,5973] [,5974]
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[9,] 1 0 1 1 1 0 0 1 0
[10,] 1 0 0 0 0 0 0 0 1
[,5975] [,5976] [,5977] [,5978] [,5979] [,5980] [,5981] [,5982] [,5983]
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[,5984] [,5985] [,5986] [,5987] [,5988] [,5989] [,5990] [,5991] [,5992]
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[,5993] [,5994] [,5995] [,5996] [,5997] [,5998] [,5999] [,6000] [,6001]
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[,6002] [,6003] [,6004] [,6005] [,6006] [,6007] [,6008] [,6009] [,6010]
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[,6011] [,6012] [,6013] [,6014] [,6015] [,6016] [,6017] [,6018] [,6019]
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[,6020] [,6021] [,6022] [,6023] [,6024] [,6025] [,6026] [,6027] [,6028]
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[,6029] [,6030] [,6031] [,6032] [,6033] [,6034] [,6035] [,6036] [,6037]
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[,6038] [,6039] [,6040] [,6041] [,6042] [,6043] [,6044] [,6045] [,6046]
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[,6047] [,6048] [,6049] [,6050] [,6051] [,6052] [,6053] [,6054] [,6055]
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[,6056] [,6057] [,6058] [,6059] [,6060] [,6061] [,6062] [,6063] [,6064]
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[,6065] [,6066] [,6067] [,6068] [,6069] [,6070] [,6071] [,6072] [,6073]
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[,6074] [,6075] [,6076] [,6077] [,6078] [,6079] [,6080] [,6081] [,6082]
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[,6083] [,6084] [,6085] [,6086] [,6087] [,6088] [,6089] [,6090] [,6091]
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[,6092] [,6093] [,6094] [,6095] [,6096] [,6097] [,6098] [,6099] [,6100]
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[,6101] [,6102] [,6103] [,6104] [,6105] [,6106] [,6107] [,6108] [,6109]
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[,6110] [,6111] [,6112] [,6113] [,6114] [,6115] [,6116] [,6117] [,6118]
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[,6119] [,6120] [,6121] [,6122] [,6123] [,6124] [,6125] [,6126] [,6127]
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[,6128] [,6129] [,6130] [,6131] [,6132] [,6133] [,6134] [,6135] [,6136]
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[,6137] [,6138] [,6139] [,6140] [,6141] [,6142] [,6143] [,6144] [,6145]
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[,6146] [,6147] [,6148] [,6149] [,6150] [,6151] [,6152] [,6153] [,6154]
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[,6155] [,6156] [,6157] [,6158] [,6159] [,6160] [,6161] [,6162] [,6163]
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[,6164] [,6165] [,6166] [,6167] [,6168] [,6169] [,6170] [,6171] [,6172]
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[,6173] [,6174] [,6175] [,6176] [,6177] [,6178] [,6179] [,6180] [,6181]
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[,6182] [,6183] [,6184] [,6185] [,6186] [,6187] [,6188] [,6189] [,6190]
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[,6191] [,6192] [,6193] [,6194] [,6195] [,6196] [,6197] [,6198] [,6199]
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[,6200] [,6201] [,6202] [,6203] [,6204] [,6205] [,6206] [,6207] [,6208]
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[,6209] [,6210] [,6211] [,6212] [,6213] [,6214] [,6215] [,6216] [,6217]
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[,6218] [,6219] [,6220] [,6221] [,6222] [,6223] [,6224] [,6225] [,6226]
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[,6227] [,6228] [,6229] [,6230] [,6231] [,6232] [,6233] [,6234] [,6235]
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[,6236] [,6237] [,6238] [,6239] [,6240] [,6241] [,6242] [,6243] [,6244]
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[,6245] [,6246] [,6247] [,6248] [,6249] [,6250] [,6251] [,6252] [,6253]
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[,6254] [,6255] [,6256] [,6257] [,6258] [,6259] [,6260] [,6261] [,6262]
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[,6263] [,6264] [,6265] [,6266] [,6267] [,6268] [,6269] [,6270] [,6271]
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[,6272] [,6273] [,6274] [,6275] [,6276] [,6277] [,6278] [,6279] [,6280]
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[,6281] [,6282] [,6283] [,6284] [,6285] [,6286] [,6287] [,6288] [,6289]
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[,6290] [,6291] [,6292] [,6293] [,6294] [,6295] [,6296] [,6297] [,6298]
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[,6299] [,6300] [,6301] [,6302] [,6303] [,6304] [,6305] [,6306] [,6307]
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[,6308] [,6309] [,6310] [,6311] [,6312] [,6313] [,6314] [,6315] [,6316]
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[,6317] [,6318] [,6319] [,6320] [,6321] [,6322] [,6323] [,6324] [,6325]
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[,6326] [,6327] [,6328] [,6329] [,6330] [,6331] [,6332] [,6333] [,6334]
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[,6335] [,6336] [,6337] [,6338] [,6339] [,6340] [,6341] [,6342] [,6343]
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[,6344] [,6345] [,6346] [,6347] [,6348] [,6349] [,6350] [,6351] [,6352]
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[,6353] [,6354] [,6355] [,6356] [,6357] [,6358] [,6359] [,6360] [,6361]
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[,6362] [,6363] [,6364] [,6365] [,6366] [,6367] [,6368] [,6369] [,6370]
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[,6371] [,6372] [,6373] [,6374] [,6375] [,6376] [,6377] [,6378] [,6379]
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[,6380] [,6381] [,6382] [,6383] [,6384] [,6385] [,6386] [,6387] [,6388]
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[,6389] [,6390] [,6391] [,6392] [,6393] [,6394] [,6395] [,6396] [,6397]
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[,6398] [,6399] [,6400] [,6401] [,6402] [,6403] [,6404] [,6405] [,6406]
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[,6407] [,6408] [,6409] [,6410] [,6411] [,6412] [,6413] [,6414] [,6415]
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[,6416] [,6417] [,6418] [,6419] [,6420] [,6421] [,6422] [,6423] [,6424]
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[,6425] [,6426] [,6427] [,6428] [,6429] [,6430] [,6431] [,6432] [,6433]
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[,6434] [,6435] [,6436] [,6437] [,6438] [,6439] [,6440] [,6441] [,6442]
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[,6443] [,6444] [,6445] [,6446] [,6447] [,6448] [,6449] [,6450] [,6451]
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[,6452] [,6453] [,6454] [,6455] [,6456] [,6457] [,6458] [,6459] [,6460]
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[,6461] [,6462] [,6463] [,6464] [,6465] [,6466] [,6467] [,6468] [,6469]
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[,6470] [,6471] [,6472] [,6473] [,6474] [,6475] [,6476] [,6477] [,6478]
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[,6479] [,6480] [,6481] [,6482] [,6483] [,6484] [,6485] [,6486] [,6487]
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[,6488] [,6489] [,6490] [,6491] [,6492] [,6493] [,6494] [,6495] [,6496]
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[,6497] [,6498] [,6499] [,6500] [,6501] [,6502] [,6503] [,6504] [,6505]
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[,6506] [,6507] [,6508] [,6509] [,6510] [,6511] [,6512] [,6513] [,6514]
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[,6515] [,6516] [,6517] [,6518] [,6519] [,6520] [,6521] [,6522] [,6523]
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[,6524] [,6525] [,6526] [,6527] [,6528] [,6529] [,6530] [,6531] [,6532]
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[,6533] [,6534] [,6535] [,6536] [,6537] [,6538] [,6539] [,6540] [,6541]
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[,6542] [,6543] [,6544] [,6545] [,6546] [,6547] [,6548] [,6549] [,6550]
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[,6551] [,6552] [,6553] [,6554] [,6555] [,6556] [,6557] [,6558] [,6559]
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[,6560] [,6561] [,6562] [,6563] [,6564] [,6565] [,6566] [,6567] [,6568]
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[,6569] [,6570] [,6571] [,6572] [,6573] [,6574] [,6575] [,6576] [,6577]
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[,6578] [,6579] [,6580] [,6581] [,6582] [,6583] [,6584] [,6585] [,6586]
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[,6587] [,6588] [,6589] [,6590] [,6591] [,6592] [,6593] [,6594] [,6595]
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[,6596] [,6597] [,6598] [,6599] [,6600] [,6601] [,6602] [,6603] [,6604]
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[,6605] [,6606] [,6607] [,6608] [,6609] [,6610] [,6611] [,6612] [,6613]
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[,6614] [,6615] [,6616] [,6617] [,6618] [,6619] [,6620] [,6621] [,6622]
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[,6623] [,6624] [,6625] [,6626] [,6627] [,6628] [,6629] [,6630] [,6631]
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[,6632] [,6633] [,6634] [,6635] [,6636] [,6637] [,6638] [,6639] [,6640]
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[,6641] [,6642] [,6643] [,6644] [,6645] [,6646] [,6647] [,6648] [,6649]
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[,6650] [,6651] [,6652] [,6653] [,6654] [,6655] [,6656] [,6657] [,6658]
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[,6659] [,6660] [,6661] [,6662] [,6663] [,6664] [,6665] [,6666] [,6667]
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[,6668] [,6669] [,6670] [,6671] [,6672] [,6673] [,6674] [,6675] [,6676]
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[,6677] [,6678] [,6679] [,6680] [,6681] [,6682] [,6683] [,6684] [,6685]
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[,6686] [,6687] [,6688] [,6689] [,6690] [,6691] [,6692] [,6693] [,6694]
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[,6695] [,6696] [,6697] [,6698] [,6699] [,6700] [,6701] [,6702] [,6703]
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[,6704] [,6705] [,6706] [,6707] [,6708] [,6709] [,6710] [,6711] [,6712]
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[,6713] [,6714] [,6715] [,6716] [,6717] [,6718] [,6719] [,6720] [,6721]
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[,6722] [,6723] [,6724] [,6725] [,6726] [,6727] [,6728] [,6729] [,6730]
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[,6731] [,6732] [,6733] [,6734] [,6735] [,6736] [,6737] [,6738] [,6739]
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[,6740] [,6741] [,6742] [,6743] [,6744] [,6745] [,6746] [,6747] [,6748]
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[6,] 0 0 0 0 1 0 1 0 1
[7,] 0 0 0 0 0 0 0 0 1
[8,] 0 0 0 0 0 1 0 0 0
[9,] 0 0 0 1 0 0 0 1 0
[10,] 0 1 0 1 0 0 0 0 0
[,6749] [,6750] [,6751] [,6752] [,6753] [,6754] [,6755] [,6756] [,6757]
[1,] 1 0 0 0 0 1 0 0 1
[2,] 1 0 0 1 0 0 0 0 1
[3,] 1 1 0 0 0 1 0 0 0
[4,] 0 1 0 1 0 0 0 1 0
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[6,] 1 0 0 0 0 0 1 0 0
[7,] 1 0 0 0 0 1 0 1 0
[8,] 1 1 1 0 0 0 1 0 0
[9,] 1 0 0 1 0 1 0 0 0
[10,] 0 1 0 1 0 1 0 1 1
[,6758] [,6759] [,6760] [,6761] [,6762] [,6763] [,6764] [,6765] [,6766]
[1,] 1 0 1 0 0 0 0 0 1
[2,] 1 0 0 0 1 0 0 0 0
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[4,] 0 0 0 0 0 1 1 0 0
[5,] 0 0 0 1 0 0 0 1 0
[6,] 0 1 0 1 0 0 0 0 0
[7,] 0 0 0 0 0 0 0 0 0
[8,] 1 0 0 1 0 1 0 1 0
[9,] 0 1 0 1 0 1 0 1 0
[10,] 0 0 0 0 0 0 1 0 0
[,6767] [,6768] [,6769] [,6770] [,6771] [,6772] [,6773] [,6774] [,6775]
[1,] 0 1 1 1 0 0 1 0 0
[2,] 0 0 1 0 1 0 1 0 0
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[4,] 1 1 1 0 0 1 0 0 0
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[6,] 1 0 0 0 0 0 0 0 0
[7,] 0 0 0 0 0 0 0 1 0
[8,] 1 0 1 0 0 0 0 1 0
[9,] 0 1 1 0 0 0 0 0 0
[10,] 1 0 1 0 0 0 0 0 0
[,6776] [,6777] [,6778] [,6779] [,6780] [,6781] [,6782] [,6783] [,6784]
[1,] 1 0 0 0 0 1 0 1 0
[2,] 0 0 0 1 0 0 0 0 0
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[8,] 0 0 1 0 0 0 0 0 1
[9,] 0 0 0 0 0 0 0 1 0
[10,] 0 0 1 1 0 0 0 0 1
[,6785] [,6786] [,6787] [,6788] [,6789] [,6790] [,6791] [,6792] [,6793]
[1,] 0 0 0 0 0 0 0 0 0
[2,] 1 0 1 0 0 0 1 0 0
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[7,] 1 0 1 1 0 0 0 0 0
[8,] 0 1 0 0 0 0 0 1 0
[9,] 0 0 1 0 1 0 0 0 0
[10,] 0 0 0 0 0 0 0 0 1
[,6794] [,6795] [,6796] [,6797] [,6798] [,6799] [,6800] [,6801] [,6802]
[1,] 0 0 0 0 1 0 0 1 1
[2,] 1 0 1 0 1 0 0 1 1
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[8,] 0 0 1 0 0 0 0 1 0
[9,] 0 0 0 0 0 0 0 0 0
[10,] 1 0 0 1 0 0 0 0 0
[,6803] [,6804] [,6805] [,6806] [,6807] [,6808] [,6809] [,6810] [,6811]
[1,] 0 1 0 1 0 0 0 0 0
[2,] 0 1 1 0 0 0 0 0 0
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[8,] 0 0 1 1 0 0 0 0 0
[9,] 0 0 1 1 0 0 0 0 0
[10,] 0 1 1 0 1 0 1 1 1
[,6812] [,6813] [,6814] [,6815] [,6816] [,6817] [,6818] [,6819] [,6820]
[1,] 0 0 0 0 0 0 0 0 1
[2,] 0 1 0 1 1 1 0 0 0
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[4,] 0 1 0 0 0 1 0 1 0
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[6,] 0 0 0 0 1 1 0 0 0
[7,] 0 0 0 0 0 0 0 0 0
[8,] 0 0 0 0 1 0 0 0 0
[9,] 0 0 0 0 0 0 0 1 0
[10,] 0 0 0 0 0 0 0 0 1
[,6821] [,6822] [,6823] [,6824] [,6825] [,6826] [,6827] [,6828] [,6829]
[1,] 0 1 1 0 1 0 0 0 0
[2,] 0 0 1 0 0 1 0 0 0
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[6,] 0 0 0 1 0 0 0 0 0
[7,] 0 1 0 0 0 1 1 0 0
[8,] 0 0 1 0 0 0 0 0 0
[9,] 1 0 0 1 0 1 0 0 1
[10,] 1 0 1 0 0 0 0 0 0
[,6830] [,6831] [,6832] [,6833] [,6834] [,6835] [,6836] [,6837] [,6838]
[1,] 1 0 0 0 0 0 1 0 1
[2,] 0 0 1 0 1 0 0 0 1
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[7,] 1 0 1 1 1 1 0 1 0
[8,] 1 0 1 0 0 0 0 0 0
[9,] 0 0 0 0 0 0 0 0 0
[10,] 0 1 0 0 0 1 1 1 1
[,6839] [,6840] [,6841] [,6842] [,6843] [,6844] [,6845] [,6846] [,6847]
[1,] 0 1 0 0 1 0 0 1 0
[2,] 0 0 0 0 0 0 0 0 0
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[6,] 0 0 0 0 0 0 1 0 1
[7,] 0 0 0 0 0 0 1 0 0
[8,] 1 1 0 0 0 1 0 0 0
[9,] 0 1 0 0 0 0 0 0 0
[10,] 0 0 1 0 1 1 0 0 0
[,6848] [,6849] [,6850] [,6851] [,6852] [,6853] [,6854] [,6855] [,6856]
[1,] 0 0 1 0 1 0 0 0 0
[2,] 0 0 0 0 0 0 1 0 1
[3,] 0 0 1 0 0 1 0 1 0
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[6,] 1 0 0 0 1 0 1 0 0
[7,] 0 0 0 0 0 1 0 0 1
[8,] 0 1 0 0 1 0 1 0 0
[9,] 0 0 0 0 0 0 1 0 0
[10,] 0 1 0 0 1 0 1 0 0
[,6857] [,6858] [,6859] [,6860] [,6861] [,6862] [,6863] [,6864] [,6865]
[1,] 1 1 0 1 1 0 0 0 1
[2,] 0 1 0 0 0 0 0 0 0
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[7,] 0 0 0 0 1 1 0 0 1
[8,] 0 0 0 1 0 0 0 0 0
[9,] 0 1 0 0 1 1 0 0 0
[10,] 0 0 1 1 0 0 0 0 0
[,6866] [,6867] [,6868] [,6869] [,6870] [,6871] [,6872] [,6873] [,6874]
[1,] 0 0 0 1 1 0 0 0 0
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[7,] 0 1 0 1 0 0 0 0 0
[8,] 0 1 0 0 0 1 0 0 0
[9,] 1 1 0 1 0 0 0 1 0
[10,] 0 0 0 0 0 0 1 0 1
[,6875] [,6876] [,6877] [,6878] [,6879] [,6880] [,6881] [,6882] [,6883]
[1,] 0 0 0 0 0 1 1 1 0
[2,] 0 1 1 0 0 0 0 0 0
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[7,] 0 0 1 0 0 0 0 0 1
[8,] 0 1 0 1 1 1 1 1 0
[9,] 0 1 0 0 0 0 1 0 1
[10,] 0 0 0 1 0 1 0 0 1
[,6884] [,6885] [,6886] [,6887] [,6888] [,6889] [,6890] [,6891] [,6892]
[1,] 1 0 0 0 0 1 0 1 0
[2,] 0 1 0 0 0 0 0 0 0
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[7,] 0 0 1 0 0 1 0 0 0
[8,] 0 1 0 1 0 1 0 1 0
[9,] 0 0 1 0 0 0 1 0 0
[10,] 0 0 0 0 1 1 1 1 0
[,6893] [,6894] [,6895] [,6896] [,6897] [,6898] [,6899] [,6900] [,6901]
[1,] 0 0 1 0 0 0 1 0 0
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[8,] 0 1 0 0 0 0 0 0 1
[9,] 0 0 0 0 0 0 0 0 0
[10,] 0 0 0 0 0 0 0 1 0
[,6902] [,6903] [,6904] [,6905] [,6906] [,6907] [,6908] [,6909] [,6910]
[1,] 1 1 0 0 0 0 0 0 0
[2,] 1 0 0 0 1 0 1 0 1
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[5,] 1 0 0 1 0 0 1 1 0
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[7,] 0 0 0 0 0 0 0 0 1
[8,] 1 1 0 0 0 1 0 0 0
[9,] 0 1 0 0 1 0 0 0 1
[10,] 1 0 0 0 0 0 0 1 0
[,6911] [,6912] [,6913] [,6914] [,6915] [,6916] [,6917] [,6918] [,6919]
[1,] 0 0 0 0 0 0 0 0 0
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[7,] 0 0 0 0 0 0 0 0 0
[8,] 1 0 0 0 0 1 0 1 1
[9,] 1 1 0 0 0 0 0 0 0
[10,] 0 0 1 0 1 0 0 1 0
[,6920] [,6921] [,6922] [,6923] [,6924] [,6925] [,6926] [,6927] [,6928]
[1,] 0 1 1 0 0 0 0 0 0
[2,] 0 1 0 0 0 1 0 0 1
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[5,] 0 1 0 0 1 0 1 1 0
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[7,] 0 0 0 1 1 0 0 1 0
[8,] 1 0 0 1 0 0 1 0 0
[9,] 1 0 0 0 0 0 0 0 0
[10,] 0 0 1 0 0 0 1 1 0
[,6929] [,6930] [,6931] [,6932] [,6933] [,6934] [,6935] [,6936] [,6937]
[1,] 0 0 1 0 0 1 0 0 1
[2,] 1 0 1 1 0 0 0 0 0
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[5,] 0 1 0 0 1 0 0 1 0
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[7,] 0 0 0 0 1 0 0 1 1
[8,] 1 0 0 0 0 0 0 0 0
[9,] 1 0 0 0 0 0 0 0 0
[10,] 0 1 1 0 0 0 0 0 0
[,6938] [,6939] [,6940] [,6941] [,6942] [,6943] [,6944] [,6945] [,6946]
[1,] 0 0 0 0 0 0 0 0 0
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[6,] 0 0 0 1 0 0 1 1 1
[7,] 0 0 0 0 0 0 0 1 0
[8,] 1 0 0 0 0 0 0 1 0
[9,] 0 1 0 0 0 1 0 1 0
[10,] 0 1 0 0 1 0 0 1 0
[,6947] [,6948] [,6949] [,6950] [,6951] [,6952] [,6953] [,6954] [,6955]
[1,] 0 0 0 0 0 0 0 1 1
[2,] 0 0 0 1 1 0 0 0 0
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[7,] 1 0 0 0 0 1 0 0 1
[8,] 0 0 0 0 1 0 0 0 0
[9,] 0 1 0 1 1 1 0 0 0
[10,] 0 0 0 1 0 1 0 0 1
[,6956] [,6957] [,6958] [,6959] [,6960] [,6961] [,6962] [,6963] [,6964]
[1,] 1 0 0 0 0 0 0 0 0
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[7,] 0 0 0 0 1 0 0 0 0
[8,] 0 1 0 1 0 0 1 0 0
[9,] 0 0 1 0 0 0 0 0 0
[10,] 1 1 0 1 0 0 0 0 0
[,6965] [,6966] [,6967] [,6968] [,6969] [,6970] [,6971] [,6972] [,6973]
[1,] 0 0 0 0 1 1 0 0 0
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[7,] 0 0 0 0 0 0 0 1 1
[8,] 0 0 0 0 0 1 0 1 0
[9,] 1 1 1 0 1 1 0 1 0
[10,] 1 1 0 0 0 0 0 0 1
[,6974] [,6975] [,6976] [,6977] [,6978] [,6979] [,6980] [,6981] [,6982]
[1,] 0 0 0 0 0 0 0 1 0
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[8,] 0 0 1 0 1 0 0 1 0
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[10,] 1 0 1 0 0 1 0 0 1
[,6983] [,6984] [,6985] [,6986] [,6987] [,6988] [,6989] [,6990] [,6991]
[1,] 0 0 0 0 0 1 0 0 0
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[10,] 0 0 0 0 0 1 1 0 0
[,6992] [,6993] [,6994] [,6995] [,6996] [,6997] [,6998] [,6999] [,7000]
[1,] 0 0 0 0 0 1 0 1 0
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[,7001] [,7002] [,7003] [,7004] [,7005] [,7006] [,7007] [,7008] [,7009]
[1,] 0 1 0 0 0 0 0 0 0
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[10,] 1 0 0 1 0 0 1 0 1
[,7010] [,7011] [,7012] [,7013] [,7014] [,7015] [,7016] [,7017] [,7018]
[1,] 0 0 0 1 0 1 0 1 0
[2,] 1 0 0 0 0 0 1 0 1
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[7,] 0 0 0 0 0 1 1 0 1
[8,] 0 0 0 0 0 0 0 1 0
[9,] 1 1 1 0 0 0 1 0 0
[10,] 0 0 1 0 1 0 0 1 0
[,7019] [,7020] [,7021] [,7022] [,7023] [,7024] [,7025] [,7026] [,7027]
[1,] 0 1 0 1 0 0 0 0 1
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[7,] 0 1 1 0 0 1 1 1 0
[8,] 0 0 0 0 0 0 1 1 0
[9,] 0 0 0 1 0 0 1 1 0
[10,] 0 1 0 0 0 0 0 0 0
[,7028] [,7029] [,7030] [,7031] [,7032] [,7033] [,7034] [,7035] [,7036]
[1,] 0 0 0 0 0 0 1 1 1
[2,] 0 1 1 0 1 0 1 0 0
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[8,] 1 1 1 0 0 1 0 0 0
[9,] 0 1 1 1 0 0 1 0 0
[10,] 0 0 0 0 0 0 1 0 1
[,7037] [,7038] [,7039] [,7040] [,7041] [,7042] [,7043] [,7044] [,7045]
[1,] 1 0 1 0 1 0 0 0 0
[2,] 0 0 1 0 0 0 1 1 0
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[9,] 0 1 0 0 0 0 0 0 0
[10,] 0 0 0 0 0 0 0 1 0
[,7046] [,7047] [,7048] [,7049] [,7050] [,7051] [,7052] [,7053] [,7054]
[1,] 0 0 0 0 0 0 0 1 0
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[10,] 0 0 0 0 0 0 0 0 1
[,7055] [,7056] [,7057] [,7058] [,7059] [,7060] [,7061] [,7062] [,7063]
[1,] 0 0 0 0 0 0 0 0 0
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[10,] 1 1 1 0 0 0 0 1 0
[,7064] [,7065] [,7066] [,7067] [,7068] [,7069] [,7070] [,7071] [,7072]
[1,] 1 1 1 0 0 0 1 1 0
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[,7073] [,7074] [,7075] [,7076] [,7077] [,7078] [,7079] [,7080] [,7081]
[1,] 1 0 0 0 0 0 1 0 1
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[10,] 0 1 0 0 1 0 0 0 0
[,7082] [,7083] [,7084] [,7085] [,7086] [,7087] [,7088] [,7089] [,7090]
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[,7091] [,7092] [,7093] [,7094] [,7095] [,7096] [,7097] [,7098] [,7099]
[1,] 0 1 0 0 1 1 0 0 1
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[,7100] [,7101] [,7102] [,7103] [,7104] [,7105] [,7106] [,7107] [,7108]
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[,7109] [,7110] [,7111] [,7112] [,7113] [,7114] [,7115] [,7116] [,7117]
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[,7118] [,7119] [,7120] [,7121] [,7122] [,7123] [,7124] [,7125] [,7126]
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[,7127] [,7128] [,7129] [,7130] [,7131] [,7132] [,7133] [,7134] [,7135]
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[,7136] [,7137] [,7138] [,7139] [,7140] [,7141] [,7142] [,7143] [,7144]
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[,7145] [,7146] [,7147] [,7148] [,7149] [,7150] [,7151] [,7152] [,7153]
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[,7154] [,7155] [,7156] [,7157] [,7158] [,7159] [,7160] [,7161] [,7162]
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[,7190] [,7191] [,7192] [,7193] [,7194] [,7195] [,7196] [,7197] [,7198]
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[,7199] [,7200] [,7201] [,7202] [,7203] [,7204] [,7205] [,7206] [,7207]
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[,7208] [,7209] [,7210] [,7211] [,7212] [,7213] [,7214] [,7215] [,7216]
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[,7217] [,7218] [,7219] [,7220] [,7221] [,7222] [,7223] [,7224] [,7225]
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[,7226] [,7227] [,7228] [,7229] [,7230] [,7231] [,7232] [,7233] [,7234]
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[,7235] [,7236] [,7237] [,7238] [,7239] [,7240] [,7241] [,7242] [,7243]
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[,7244] [,7245] [,7246] [,7247] [,7248] [,7249] [,7250] [,7251] [,7252]
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[,7253] [,7254] [,7255] [,7256] [,7257] [,7258] [,7259] [,7260] [,7261]
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[,7262] [,7263] [,7264] [,7265] [,7266] [,7267] [,7268] [,7269] [,7270]
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[,7271] [,7272] [,7273] [,7274] [,7275] [,7276] [,7277] [,7278] [,7279]
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[,7280] [,7281] [,7282] [,7283] [,7284] [,7285] [,7286] [,7287] [,7288]
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[,7289] [,7290] [,7291] [,7292] [,7293] [,7294] [,7295] [,7296] [,7297]
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[,7298] [,7299] [,7300] [,7301] [,7302] [,7303] [,7304] [,7305] [,7306]
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[,7307] [,7308] [,7309] [,7310] [,7311] [,7312] [,7313] [,7314] [,7315]
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[,7316] [,7317] [,7318] [,7319] [,7320] [,7321] [,7322] [,7323] [,7324]
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[,7325] [,7326] [,7327] [,7328] [,7329] [,7330] [,7331] [,7332] [,7333]
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[,7334] [,7335] [,7336] [,7337] [,7338] [,7339] [,7340] [,7341] [,7342]
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[,7343] [,7344] [,7345] [,7346] [,7347] [,7348] [,7349] [,7350] [,7351]
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[,7352] [,7353] [,7354] [,7355] [,7356] [,7357] [,7358] [,7359] [,7360]
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[,7361] [,7362] [,7363] [,7364] [,7365] [,7366] [,7367] [,7368] [,7369]
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[,7370] [,7371] [,7372] [,7373] [,7374] [,7375] [,7376] [,7377] [,7378]
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[,7379] [,7380] [,7381] [,7382] [,7383] [,7384] [,7385] [,7386] [,7387]
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[,7388] [,7389] [,7390] [,7391] [,7392] [,7393] [,7394] [,7395] [,7396]
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[,7397] [,7398] [,7399] [,7400] [,7401] [,7402] [,7403] [,7404] [,7405]
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[,7406] [,7407] [,7408] [,7409] [,7410] [,7411] [,7412] [,7413] [,7414]
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[,7415] [,7416] [,7417] [,7418] [,7419] [,7420] [,7421] [,7422] [,7423]
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[,7424] [,7425] [,7426] [,7427] [,7428] [,7429] [,7430] [,7431] [,7432]
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[,7433] [,7434] [,7435] [,7436] [,7437] [,7438] [,7439] [,7440] [,7441]
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[,7442] [,7443] [,7444] [,7445] [,7446] [,7447] [,7448] [,7449] [,7450]
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[,7451] [,7452] [,7453] [,7454] [,7455] [,7456] [,7457] [,7458] [,7459]
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[,7460] [,7461] [,7462] [,7463] [,7464] [,7465] [,7466] [,7467] [,7468]
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[,7469] [,7470] [,7471] [,7472] [,7473] [,7474] [,7475] [,7476] [,7477]
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[,7478] [,7479] [,7480] [,7481] [,7482] [,7483] [,7484] [,7485] [,7486]
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[,7487] [,7488] [,7489] [,7490] [,7491] [,7492] [,7493] [,7494] [,7495]
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[,7496] [,7497] [,7498] [,7499] [,7500] [,7501] [,7502] [,7503] [,7504]
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[,7505] [,7506] [,7507] [,7508] [,7509] [,7510] [,7511] [,7512] [,7513]
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[,7514] [,7515] [,7516] [,7517] [,7518] [,7519] [,7520] [,7521] [,7522]
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[,7523] [,7524] [,7525] [,7526] [,7527] [,7528] [,7529] [,7530] [,7531]
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[,7532] [,7533] [,7534] [,7535] [,7536] [,7537] [,7538] [,7539] [,7540]
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[,7541] [,7542] [,7543] [,7544] [,7545] [,7546] [,7547] [,7548] [,7549]
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[,7550] [,7551] [,7552] [,7553] [,7554] [,7555] [,7556] [,7557] [,7558]
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[,7559] [,7560] [,7561] [,7562] [,7563] [,7564] [,7565] [,7566] [,7567]
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[,7568] [,7569] [,7570] [,7571] [,7572] [,7573] [,7574] [,7575] [,7576]
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[,7577] [,7578] [,7579] [,7580] [,7581] [,7582] [,7583] [,7584] [,7585]
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[,7586] [,7587] [,7588] [,7589] [,7590] [,7591] [,7592] [,7593] [,7594]
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[,7595] [,7596] [,7597] [,7598] [,7599] [,7600] [,7601] [,7602] [,7603]
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[,7604] [,7605] [,7606] [,7607] [,7608] [,7609] [,7610] [,7611] [,7612]
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[,7613] [,7614] [,7615] [,7616] [,7617] [,7618] [,7619] [,7620] [,7621]
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[,7622] [,7623] [,7624] [,7625] [,7626] [,7627] [,7628] [,7629] [,7630]
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[,7631] [,7632] [,7633] [,7634] [,7635] [,7636] [,7637] [,7638] [,7639]
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[,7640] [,7641] [,7642] [,7643] [,7644] [,7645] [,7646] [,7647] [,7648]
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[,7649] [,7650] [,7651] [,7652] [,7653] [,7654] [,7655] [,7656] [,7657]
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[,7658] [,7659] [,7660] [,7661] [,7662] [,7663] [,7664] [,7665] [,7666]
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[,7667] [,7668] [,7669] [,7670] [,7671] [,7672] [,7673] [,7674] [,7675]
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[,7676] [,7677] [,7678] [,7679] [,7680] [,7681] [,7682] [,7683] [,7684]
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[,7685] [,7686] [,7687] [,7688] [,7689] [,7690] [,7691] [,7692] [,7693]
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[,7694] [,7695] [,7696] [,7697] [,7698] [,7699] [,7700] [,7701] [,7702]
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[,7703] [,7704] [,7705] [,7706] [,7707] [,7708] [,7709] [,7710] [,7711]
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[,7712] [,7713] [,7714] [,7715] [,7716] [,7717] [,7718] [,7719] [,7720]
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[,7721] [,7722] [,7723] [,7724] [,7725] [,7726] [,7727] [,7728] [,7729]
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[,7730] [,7731] [,7732] [,7733] [,7734] [,7735] [,7736] [,7737] [,7738]
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[,7739] [,7740] [,7741] [,7742] [,7743] [,7744] [,7745] [,7746] [,7747]
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[,7748] [,7749] [,7750] [,7751] [,7752] [,7753] [,7754] [,7755] [,7756]
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[,7757] [,7758] [,7759] [,7760] [,7761] [,7762] [,7763] [,7764] [,7765]
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[,7766] [,7767] [,7768] [,7769] [,7770] [,7771] [,7772] [,7773] [,7774]
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[,7775] [,7776] [,7777] [,7778] [,7779] [,7780] [,7781] [,7782] [,7783]
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[,7784] [,7785] [,7786] [,7787] [,7788] [,7789] [,7790] [,7791] [,7792]
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[,7793] [,7794] [,7795] [,7796] [,7797] [,7798] [,7799] [,7800] [,7801]
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[,7802] [,7803] [,7804] [,7805] [,7806] [,7807] [,7808] [,7809] [,7810]
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[,7811] [,7812] [,7813] [,7814] [,7815] [,7816] [,7817] [,7818] [,7819]
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[,7820] [,7821] [,7822] [,7823] [,7824] [,7825] [,7826] [,7827] [,7828]
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[,7829] [,7830] [,7831] [,7832] [,7833] [,7834] [,7835] [,7836] [,7837]
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[,7838] [,7839] [,7840] [,7841] [,7842] [,7843] [,7844] [,7845] [,7846]
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[,7847] [,7848] [,7849] [,7850] [,7851] [,7852] [,7853] [,7854] [,7855]
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[,7856] [,7857] [,7858] [,7859] [,7860] [,7861] [,7862] [,7863] [,7864]
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[,7865] [,7866] [,7867] [,7868] [,7869] [,7870] [,7871] [,7872] [,7873]
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[,7874] [,7875] [,7876] [,7877] [,7878] [,7879] [,7880] [,7881] [,7882]
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[,7883] [,7884] [,7885] [,7886] [,7887] [,7888] [,7889] [,7890] [,7891]
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[,7892] [,7893] [,7894] [,7895] [,7896] [,7897] [,7898] [,7899] [,7900]
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[,7901] [,7902] [,7903] [,7904] [,7905] [,7906] [,7907] [,7908] [,7909]
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[,7910] [,7911] [,7912] [,7913] [,7914] [,7915] [,7916] [,7917] [,7918]
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[,7919] [,7920] [,7921] [,7922] [,7923] [,7924] [,7925] [,7926] [,7927]
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[,7928] [,7929] [,7930] [,7931] [,7932] [,7933] [,7934] [,7935] [,7936]
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[,7937] [,7938] [,7939] [,7940] [,7941] [,7942] [,7943] [,7944] [,7945]
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[,7946] [,7947] [,7948] [,7949] [,7950] [,7951] [,7952] [,7953] [,7954]
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[,7955] [,7956] [,7957] [,7958] [,7959] [,7960] [,7961] [,7962] [,7963]
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[,7964] [,7965] [,7966] [,7967] [,7968] [,7969] [,7970] [,7971] [,7972]
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[,7973] [,7974] [,7975] [,7976] [,7977] [,7978] [,7979] [,7980] [,7981]
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[,7982] [,7983] [,7984] [,7985] [,7986] [,7987] [,7988] [,7989] [,7990]
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[,7991] [,7992] [,7993] [,7994] [,7995] [,7996] [,7997] [,7998] [,7999]
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[,8000] [,8001] [,8002] [,8003] [,8004] [,8005] [,8006] [,8007] [,8008]
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[,8009] [,8010] [,8011] [,8012] [,8013] [,8014] [,8015] [,8016] [,8017]
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[,8018] [,8019] [,8020] [,8021] [,8022] [,8023] [,8024] [,8025] [,8026]
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[,8027] [,8028] [,8029] [,8030] [,8031] [,8032] [,8033] [,8034] [,8035]
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[,8036] [,8037] [,8038] [,8039] [,8040] [,8041] [,8042] [,8043] [,8044]
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[,8045] [,8046] [,8047] [,8048] [,8049] [,8050] [,8051] [,8052] [,8053]
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[,8054] [,8055] [,8056] [,8057] [,8058] [,8059] [,8060] [,8061] [,8062]
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[,8063] [,8064] [,8065] [,8066] [,8067] [,8068] [,8069] [,8070] [,8071]
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[,8072] [,8073] [,8074] [,8075] [,8076] [,8077] [,8078] [,8079] [,8080]
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[,8081] [,8082] [,8083] [,8084] [,8085] [,8086] [,8087] [,8088] [,8089]
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[,8090] [,8091] [,8092] [,8093] [,8094] [,8095] [,8096] [,8097] [,8098]
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[,8099] [,8100] [,8101] [,8102] [,8103] [,8104] [,8105] [,8106] [,8107]
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[,8108] [,8109] [,8110] [,8111] [,8112] [,8113] [,8114] [,8115] [,8116]
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[,8117] [,8118] [,8119] [,8120] [,8121] [,8122] [,8123] [,8124] [,8125]
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[,8126] [,8127] [,8128] [,8129] [,8130] [,8131] [,8132] [,8133] [,8134]
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[,8135] [,8136] [,8137] [,8138] [,8139] [,8140] [,8141] [,8142] [,8143]
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[,8144] [,8145] [,8146] [,8147] [,8148] [,8149] [,8150] [,8151] [,8152]
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[,8153] [,8154] [,8155] [,8156] [,8157] [,8158] [,8159] [,8160] [,8161]
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[,8162] [,8163] [,8164] [,8165] [,8166] [,8167] [,8168] [,8169] [,8170]
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[,8171] [,8172] [,8173] [,8174] [,8175] [,8176] [,8177] [,8178] [,8179]
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[,8180] [,8181] [,8182] [,8183] [,8184] [,8185] [,8186] [,8187] [,8188]
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[,8189] [,8190] [,8191] [,8192] [,8193] [,8194] [,8195] [,8196] [,8197]
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[,8198] [,8199] [,8200] [,8201] [,8202] [,8203] [,8204] [,8205] [,8206]
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[,8207] [,8208] [,8209] [,8210] [,8211] [,8212] [,8213] [,8214] [,8215]
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[,8216] [,8217] [,8218] [,8219] [,8220] [,8221] [,8222] [,8223] [,8224]
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[,8225] [,8226] [,8227] [,8228] [,8229] [,8230] [,8231] [,8232] [,8233]
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[,8234] [,8235] [,8236] [,8237] [,8238] [,8239] [,8240] [,8241] [,8242]
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[6,] 0 0 1 0 1 0 1 0 0
[7,] 0 1 1 0 0 0 0 1 0
[8,] 1 0 1 1 0 1 0 1 0
[9,] 0 0 0 0 0 1 0 0 0
[10,] 0 0 0 0 0 1 0 0 0
[,8243] [,8244] [,8245] [,8246] [,8247] [,8248] [,8249] [,8250] [,8251]
[1,] 0 1 0 1 0 0 0 0 0
[2,] 1 0 0 0 1 0 0 0 0
[3,] 0 0 0 0 0 0 1 0 0
[4,] 1 0 1 1 0 0 0 0 0
[5,] 0 0 0 1 0 0 0 0 0
[6,] 0 0 1 0 0 0 1 0 0
[7,] 0 1 0 0 0 0 0 1 1
[8,] 1 0 0 1 0 0 0 0 1
[9,] 0 0 1 0 0 1 0 0 0
[10,] 0 0 0 0 0 0 1 0 0
[,8252] [,8253] [,8254] [,8255] [,8256] [,8257] [,8258] [,8259] [,8260]
[1,] 1 0 0 0 1 0 0 0 1
[2,] 0 1 0 0 0 1 0 0 0
[3,] 0 1 1 0 0 0 1 0 0
[4,] 0 0 0 0 1 0 0 1 0
[5,] 0 0 0 0 0 1 0 0 1
[6,] 0 0 0 0 0 0 0 1 0
[7,] 0 1 1 0 0 0 0 0 0
[8,] 0 1 0 0 0 0 0 0 0
[9,] 1 0 0 0 0 0 1 0 0
[10,] 0 0 0 0 0 0 1 0 1
[,8261] [,8262] [,8263] [,8264] [,8265] [,8266] [,8267] [,8268] [,8269]
[1,] 0 0 0 0 0 0 1 0 0
[2,] 0 0 0 0 1 0 0 0 0
[3,] 1 0 0 0 1 1 0 1 0
[4,] 0 0 1 0 0 1 0 1 0
[5,] 1 0 0 1 0 0 0 0 0
[6,] 0 1 0 0 0 0 1 0 0
[7,] 0 1 0 0 0 0 0 1 0
[8,] 1 1 1 0 1 0 0 0 0
[9,] 1 0 1 1 0 0 0 0 0
[10,] 0 0 1 0 0 1 1 1 0
[,8270] [,8271] [,8272] [,8273] [,8274] [,8275] [,8276] [,8277] [,8278]
[1,] 1 0 1 1 1 1 1 0 1
[2,] 0 0 0 0 1 0 0 1 0
[3,] 0 1 0 0 0 0 1 1 0
[4,] 0 0 0 0 0 0 0 0 1
[5,] 0 0 0 0 1 0 0 1 1
[6,] 1 0 0 1 0 0 1 0 0
[7,] 0 0 0 0 1 0 1 0 0
[8,] 0 1 0 0 1 0 0 0 0
[9,] 1 1 0 0 0 0 0 0 1
[10,] 1 1 0 0 0 0 0 1 0
[,8279] [,8280] [,8281] [,8282] [,8283] [,8284] [,8285] [,8286] [,8287]
[1,] 0 1 0 0 1 0 0 0 0
[2,] 0 0 0 0 0 0 0 0 1
[3,] 0 0 0 0 0 1 1 1 1
[4,] 0 0 0 0 1 0 0 0 0
[5,] 0 0 0 0 0 1 0 0 0
[6,] 0 1 1 0 0 0 0 0 1
[7,] 0 0 1 0 1 0 0 0 0
[8,] 0 1 0 1 0 0 0 1 1
[9,] 1 0 1 0 0 0 0 0 0
[10,] 0 0 0 0 1 0 0 0 0
[,8288] [,8289] [,8290] [,8291] [,8292] [,8293] [,8294] [,8295] [,8296]
[1,] 0 0 0 1 0 0 0 1 0
[2,] 0 0 0 0 1 0 0 0 0
[3,] 0 0 0 0 0 0 0 1 0
[4,] 0 0 1 0 0 0 1 0 0
[5,] 1 1 0 0 0 1 0 1 0
[6,] 0 0 0 0 0 1 0 0 0
[7,] 0 0 1 0 0 1 0 0 0
[8,] 0 0 0 0 1 1 1 1 1
[9,] 1 1 0 0 1 0 0 0 0
[10,] 0 0 0 1 0 0 0 0 0
[,8297] [,8298] [,8299] [,8300] [,8301] [,8302] [,8303] [,8304] [,8305]
[1,] 0 0 0 0 0 0 0 0 0
[2,] 0 0 0 0 1 0 0 0 0
[3,] 0 0 0 0 0 0 0 1 1
[4,] 0 0 1 0 0 0 1 0 0
[5,] 0 0 0 0 0 0 0 1 0
[6,] 0 1 1 0 0 1 0 0 1
[7,] 0 1 0 0 1 0 0 0 1
[8,] 0 0 0 0 0 0 0 1 0
[9,] 0 1 1 0 0 0 0 0 1
[10,] 0 0 0 0 0 0 1 1 0
[,8306] [,8307] [,8308] [,8309] [,8310] [,8311] [,8312] [,8313] [,8314]
[1,] 0 0 1 0 0 1 0 0 1
[2,] 1 0 1 0 0 0 0 0 0
[3,] 0 1 0 1 0 1 0 0 0
[4,] 0 0 1 0 0 0 0 0 1
[5,] 1 0 0 1 1 0 1 1 0
[6,] 0 0 0 0 0 0 1 0 1
[7,] 0 0 0 0 1 0 0 0 0
[8,] 0 0 1 1 1 1 1 0 1
[9,] 0 0 0 0 0 0 1 0 1
[10,] 0 0 1 1 0 1 1 0 0
[,8315] [,8316] [,8317] [,8318] [,8319] [,8320] [,8321] [,8322] [,8323]
[1,] 1 0 0 1 0 0 1 0 1
[2,] 0 0 1 1 0 1 0 1 1
[3,] 1 0 0 0 0 1 0 0 0
[4,] 0 0 1 0 0 0 0 0 1
[5,] 0 0 0 0 0 0 0 1 1
[6,] 0 0 0 0 0 0 1 0 0
[7,] 0 1 0 0 0 1 0 0 0
[8,] 1 0 0 0 0 0 0 1 0
[9,] 0 0 0 0 1 0 1 0 0
[10,] 1 1 0 0 0 0 0 0 0
[,8324] [,8325] [,8326] [,8327] [,8328] [,8329] [,8330] [,8331] [,8332]
[1,] 0 0 0 0 0 0 0 1 0
[2,] 0 0 0 0 0 1 0 1 1
[3,] 0 1 1 0 0 0 1 1 0
[4,] 0 0 0 0 0 0 0 1 1
[5,] 0 0 0 1 0 0 1 0 0
[6,] 0 0 0 0 0 0 0 0 0
[7,] 0 1 0 0 0 0 0 0 0
[8,] 0 0 0 0 0 0 0 0 0
[9,] 1 0 1 0 0 0 0 0 0
[10,] 0 1 0 0 1 1 0 1 1
[,8333] [,8334] [,8335] [,8336] [,8337] [,8338] [,8339] [,8340] [,8341]
[1,] 1 0 0 0 1 0 0 0 0
[2,] 0 1 0 0 0 0 1 0 0
[3,] 1 1 1 0 0 1 0 0 1
[4,] 0 0 1 0 0 1 0 0 0
[5,] 0 0 1 1 1 0 1 0 0
[6,] 0 0 0 1 0 0 0 0 0
[7,] 1 0 1 0 1 1 0 0 0
[8,] 0 0 0 0 0 0 1 0 0
[9,] 0 0 0 0 1 0 0 0 1
[10,] 0 0 1 0 0 0 0 0 1
[,8342] [,8343] [,8344] [,8345] [,8346] [,8347] [,8348] [,8349] [,8350]
[1,] 0 0 0 1 0 0 0 0 0
[2,] 1 0 0 1 1 1 0 0 1
[3,] 0 1 0 0 0 0 0 1 0
[4,] 0 0 1 0 0 1 0 0 1
[5,] 0 0 0 0 0 0 1 0 0
[6,] 1 0 0 0 0 1 0 1 1
[7,] 0 0 0 0 0 0 0 0 0
[8,] 1 0 1 0 0 0 0 0 0
[9,] 1 0 1 0 0 0 0 0 0
[10,] 0 0 0 0 0 1 0 0 0
[,8351] [,8352] [,8353] [,8354] [,8355] [,8356] [,8357] [,8358] [,8359]
[1,] 0 0 0 0 0 0 0 1 1
[2,] 0 0 0 0 0 1 0 0 0
[3,] 0 0 0 0 0 0 0 0 0
[4,] 0 0 0 0 0 1 0 0 0
[5,] 1 0 0 0 0 0 1 1 1
[6,] 1 0 0 0 0 0 0 0 1
[7,] 0 0 0 0 1 0 0 0 0
[8,] 1 1 0 0 0 0 0 1 1
[9,] 0 0 0 1 0 1 1 0 0
[10,] 0 0 0 0 0 0 0 1 0
[,8360] [,8361] [,8362] [,8363] [,8364] [,8365] [,8366] [,8367] [,8368]
[1,] 0 1 0 0 0 0 0 0 0
[2,] 0 0 0 0 1 0 0 1 1
[3,] 0 1 0 1 1 0 0 1 0
[4,] 0 0 1 0 0 0 0 0 0
[5,] 0 0 0 0 0 1 0 0 0
[6,] 0 1 0 0 0 1 1 0 0
[7,] 0 0 0 0 1 0 0 0 1
[8,] 1 0 1 0 0 0 1 0 1
[9,] 0 0 0 0 0 0 0 0 0
[10,] 0 0 0 1 0 0 0 1 0
[,8369] [,8370] [,8371] [,8372] [,8373] [,8374] [,8375] [,8376] [,8377]
[1,] 0 0 0 0 0 0 0 0 0
[2,] 0 0 0 1 1 1 0 0 1
[3,] 0 0 0 0 1 0 0 0 0
[4,] 0 0 1 0 0 0 0 0 0
[5,] 0 0 0 1 0 1 1 0 1
[6,] 0 1 0 1 0 0 1 0 1
[7,] 1 0 0 1 0 0 0 0 0
[8,] 0 1 0 0 0 1 1 1 0
[9,] 1 1 0 0 0 0 1 0 1
[10,] 0 0 0 0 0 0 0 0 1
[,8378] [,8379] [,8380] [,8381] [,8382] [,8383] [,8384] [,8385] [,8386]
[1,] 0 0 0 0 1 0 0 1 0
[2,] 0 1 0 0 0 0 0 0 0
[3,] 0 0 0 0 1 0 1 0 0
[4,] 0 1 0 0 0 0 0 1 1
[5,] 0 1 0 0 0 1 0 0 1
[6,] 0 0 1 0 1 1 1 0 0
[7,] 0 0 0 0 0 0 0 0 1
[8,] 1 1 0 0 1 0 0 0 1
[9,] 0 0 0 0 0 0 0 1 0
[10,] 1 0 1 1 0 0 0 1 0
[,8387] [,8388] [,8389] [,8390] [,8391] [,8392] [,8393] [,8394] [,8395]
[1,] 1 1 1 0 0 0 1 0 0
[2,] 0 0 0 1 0 0 1 0 0
[3,] 1 0 1 0 0 0 1 1 0
[4,] 0 0 0 0 0 1 0 0 0
[5,] 0 0 0 0 0 0 1 0 0
[6,] 1 0 0 0 0 0 0 0 0
[7,] 0 0 1 1 0 0 0 0 0
[8,] 0 0 1 0 0 0 1 0 0
[9,] 0 0 0 1 0 0 0 0 0
[10,] 0 0 0 0 0 0 0 0 0
[,8396] [,8397] [,8398] [,8399] [,8400] [,8401] [,8402] [,8403] [,8404]
[1,] 0 0 0 0 0 0 0 1 0
[2,] 1 1 1 0 0 0 1 1 1
[3,] 1 1 0 0 0 1 1 0 0
[4,] 1 0 0 0 0 0 0 1 0
[5,] 1 1 0 1 0 0 0 0 0
[6,] 0 0 1 0 1 0 0 1 0
[7,] 0 0 0 0 0 0 0 0 0
[8,] 1 1 0 0 0 0 0 0 1
[9,] 1 0 0 0 1 0 1 0 0
[10,] 0 1 1 1 1 0 0 0 0
[,8405] [,8406] [,8407] [,8408] [,8409] [,8410] [,8411] [,8412] [,8413]
[1,] 0 1 0 0 0 1 0 0 1
[2,] 0 0 1 1 0 0 0 0 0
[3,] 1 0 0 0 1 0 1 0 1
[4,] 0 0 0 0 0 0 0 1 1
[5,] 0 1 0 0 0 1 0 0 0
[6,] 0 0 1 1 0 1 0 0 0
[7,] 1 0 0 0 0 1 0 1 0
[8,] 0 0 0 0 1 1 0 0 0
[9,] 0 0 0 0 0 0 0 0 0
[10,] 0 0 0 0 1 0 1 0 0
[,8414] [,8415] [,8416] [,8417] [,8418] [,8419] [,8420] [,8421] [,8422]
[1,] 0 1 1 0 0 0 0 0 1
[2,] 0 0 1 0 0 0 0 0 0
[3,] 1 0 1 0 0 0 1 0 1
[4,] 0 1 1 1 0 1 1 0 1
[5,] 1 1 0 0 0 0 0 0 1
[6,] 1 0 0 0 0 0 0 0 0
[7,] 1 0 0 0 1 1 0 0 0
[8,] 1 0 1 0 0 0 0 1 0
[9,] 1 0 0 0 0 0 0 1 0
[10,] 0 0 1 0 0 0 0 0 0
[,8423] [,8424] [,8425] [,8426] [,8427] [,8428] [,8429] [,8430] [,8431]
[1,] 0 0 0 0 1 0 0 1 1
[2,] 1 0 0 0 0 0 1 1 0
[3,] 0 0 0 0 1 1 0 1 0
[4,] 0 0 1 0 0 1 0 0 1
[5,] 0 0 0 1 1 0 1 0 0
[6,] 1 0 1 1 0 1 0 1 0
[7,] 1 1 0 0 0 0 0 1 0
[8,] 0 0 0 0 0 0 1 0 1
[9,] 1 0 0 0 0 0 1 1 0
[10,] 0 0 1 0 0 0 0 1 0
[,8432] [,8433] [,8434] [,8435] [,8436] [,8437] [,8438] [,8439] [,8440]
[1,] 0 0 1 1 0 0 0 0 1
[2,] 0 0 0 0 1 0 0 0 0
[3,] 0 1 0 1 0 0 0 0 0
[4,] 0 0 1 0 0 0 0 1 1
[5,] 0 1 0 0 0 0 0 1 0
[6,] 0 1 0 1 1 1 0 0 0
[7,] 1 1 0 0 0 0 1 0 0
[8,] 0 0 0 0 0 0 0 0 0
[9,] 1 0 0 1 0 0 0 0 1
[10,] 0 0 0 0 0 0 0 1 0
[,8441] [,8442] [,8443] [,8444] [,8445] [,8446] [,8447] [,8448] [,8449]
[1,] 1 0 0 0 0 0 0 0 1
[2,] 0 0 0 1 1 1 1 0 1
[3,] 0 0 0 1 0 0 0 1 0
[4,] 0 1 0 1 0 0 0 0 1
[5,] 0 0 0 1 1 1 0 0 1
[6,] 0 0 0 0 1 0 0 0 0
[7,] 0 1 1 1 0 0 1 1 1
[8,] 1 1 0 0 1 1 1 0 0
[9,] 1 1 0 0 0 1 0 0 0
[10,] 0 1 0 0 0 0 0 0 0
[,8450] [,8451] [,8452] [,8453] [,8454] [,8455] [,8456] [,8457] [,8458]
[1,] 0 1 1 0 1 0 1 0 0
[2,] 0 0 1 1 1 0 0 0 0
[3,] 1 0 0 0 0 1 0 1 1
[4,] 1 1 0 0 0 1 0 0 0
[5,] 1 0 1 0 0 0 1 0 0
[6,] 0 1 0 0 1 0 0 0 0
[7,] 0 0 0 1 0 0 0 0 0
[8,] 1 1 0 0 0 0 1 1 0
[9,] 0 0 0 0 1 1 1 0 0
[10,] 0 0 0 0 1 0 0 0 0
[,8459] [,8460] [,8461] [,8462] [,8463] [,8464] [,8465] [,8466] [,8467]
[1,] 0 1 1 0 0 0 0 1 0
[2,] 0 0 0 0 0 1 0 0 1
[3,] 1 0 0 0 0 1 0 1 0
[4,] 0 0 1 0 1 0 1 0 0
[5,] 0 1 0 0 1 0 0 0 0
[6,] 0 0 0 0 0 0 0 0 0
[7,] 0 0 1 1 0 0 1 1 0
[8,] 1 0 0 1 0 0 0 1 0
[9,] 0 1 1 0 0 0 1 0 0
[10,] 0 0 0 1 0 1 0 0 0
[,8468] [,8469] [,8470] [,8471] [,8472] [,8473] [,8474] [,8475] [,8476]
[1,] 0 1 0 0 1 0 1 0 0
[2,] 0 1 0 0 0 0 0 0 0
[3,] 0 0 0 0 0 0 1 0 0
[4,] 0 0 1 1 1 0 0 0 0
[5,] 0 0 0 0 1 0 0 0 0
[6,] 0 1 0 0 0 0 0 0 0
[7,] 1 0 0 0 0 0 0 0 0
[8,] 0 0 1 0 0 0 1 1 0
[9,] 0 0 1 0 0 0 0 0 0
[10,] 0 0 0 0 0 0 0 0 0
[,8477] [,8478] [,8479] [,8480] [,8481] [,8482] [,8483] [,8484] [,8485]
[1,] 1 1 0 1 0 1 0 1 0
[2,] 0 1 1 0 0 0 1 0 1
[3,] 0 0 0 0 1 1 0 1 0
[4,] 1 0 0 0 0 1 1 0 0
[5,] 1 0 0 1 0 0 0 0 0
[6,] 1 1 1 0 0 0 0 0 0
[7,] 0 0 1 0 0 0 1 1 0
[8,] 0 0 1 0 1 1 0 0 0
[9,] 0 1 0 0 0 0 1 0 0
[10,] 0 1 0 0 0 0 0 0 0
[,8486] [,8487] [,8488] [,8489] [,8490] [,8491] [,8492] [,8493] [,8494]
[1,] 0 1 1 0 0 0 1 0 0
[2,] 0 0 1 0 0 1 0 1 0
[3,] 0 1 0 0 0 1 0 0 0
[4,] 0 0 1 1 1 1 0 0 0
[5,] 1 0 1 0 0 0 0 0 0
[6,] 0 0 0 1 0 0 0 1 0
[7,] 0 1 1 1 0 0 1 1 1
[8,] 0 0 0 0 0 0 0 0 0
[9,] 0 0 0 0 0 1 0 0 0
[10,] 0 1 0 0 1 0 1 0 1
[,8495] [,8496] [,8497] [,8498] [,8499] [,8500] [,8501] [,8502] [,8503]
[1,] 1 0 0 0 0 0 1 0 1
[2,] 0 1 0 1 0 0 0 1 1
[3,] 0 1 0 0 0 0 0 0 0
[4,] 1 0 0 1 0 0 1 0 0
[5,] 1 0 0 0 0 0 0 0 0
[6,] 0 0 1 0 0 0 0 1 1
[7,] 0 0 0 0 0 1 1 1 0
[8,] 1 0 0 0 0 0 1 1 1
[9,] 0 1 1 1 0 0 0 1 1
[10,] 1 1 0 0 0 0 0 0 0
[,8504] [,8505] [,8506] [,8507] [,8508] [,8509] [,8510] [,8511] [,8512]
[1,] 0 1 0 0 1 0 1 0 0
[2,] 1 0 0 1 1 0 0 0 1
[3,] 0 0 1 0 1 0 1 0 0
[4,] 1 0 0 0 0 0 0 0 0
[5,] 0 1 0 0 0 1 1 1 0
[6,] 0 1 1 0 0 0 0 1 1
[7,] 0 0 0 0 0 0 1 1 0
[8,] 0 1 1 0 0 0 1 0 1
[9,] 0 0 0 0 0 1 0 1 0
[10,] 1 1 0 0 1 0 1 0 0
[,8513] [,8514] [,8515] [,8516] [,8517] [,8518] [,8519] [,8520] [,8521]
[1,] 0 0 0 0 0 0 1 0 0
[2,] 0 0 0 0 1 0 0 0 1
[3,] 0 1 0 0 0 0 1 1 0
[4,] 0 0 0 0 1 0 0 0 0
[5,] 0 0 0 0 0 0 0 0 0
[6,] 0 1 1 1 0 0 0 0 0
[7,] 0 0 1 0 0 0 0 0 1
[8,] 0 1 0 0 1 0 0 0 0
[9,] 1 1 0 0 0 0 0 0 1
[10,] 0 1 0 1 0 1 1 1 1
[,8522] [,8523] [,8524] [,8525] [,8526] [,8527] [,8528] [,8529] [,8530]
[1,] 0 0 1 0 0 0 0 0 0
[2,] 0 1 0 0 0 0 0 0 0
[3,] 0 1 0 1 0 1 0 0 0
[4,] 0 0 0 0 0 1 0 0 1
[5,] 0 0 0 0 0 0 1 1 0
[6,] 0 1 1 1 0 1 1 0 0
[7,] 0 0 0 0 1 0 0 1 1
[8,] 0 1 0 0 0 0 1 0 0
[9,] 0 0 1 0 0 0 0 0 1
[10,] 0 0 1 0 0 0 1 0 1
[,8531] [,8532] [,8533] [,8534] [,8535] [,8536] [,8537] [,8538] [,8539]
[1,] 0 1 0 0 0 0 0 0 0
[2,] 1 0 0 0 1 0 0 1 0
[3,] 1 0 0 1 0 0 0 0 0
[4,] 0 0 0 0 0 0 0 0 1
[5,] 1 0 0 0 1 0 0 0 0
[6,] 0 0 0 0 1 0 1 0 1
[7,] 1 1 0 0 0 0 0 0 0
[8,] 0 1 0 0 0 0 0 0 1
[9,] 1 0 0 0 0 0 0 1 0
[10,] 0 0 0 0 0 1 0 1 0
[,8540] [,8541] [,8542] [,8543] [,8544] [,8545] [,8546] [,8547] [,8548]
[1,] 0 0 0 1 0 0 0 0 0
[2,] 0 0 0 0 1 1 0 0 0
[3,] 0 0 0 0 1 0 0 1 0
[4,] 0 0 1 1 0 0 0 0 1
[5,] 0 1 0 0 0 0 1 0 0
[6,] 1 0 1 0 0 0 0 1 1
[7,] 0 0 1 0 0 0 1 0 0
[8,] 0 0 0 0 0 0 0 0 0
[9,] 0 0 0 1 1 1 1 0 1
[10,] 0 0 0 0 0 0 0 0 1
[,8549] [,8550] [,8551] [,8552] [,8553] [,8554] [,8555] [,8556] [,8557]
[1,] 0 0 1 1 0 0 0 0 0
[2,] 0 0 0 0 0 0 1 1 0
[3,] 0 0 0 1 0 0 0 0 0
[4,] 1 1 0 0 0 0 0 0 0
[5,] 0 0 0 1 0 0 0 1 0
[6,] 0 1 0 0 0 0 1 0 0
[7,] 0 1 0 1 0 1 0 0 0
[8,] 0 0 0 1 0 0 0 0 1
[9,] 0 0 0 0 0 0 1 0 0
[10,] 0 1 0 1 1 1 1 0 1
[,8558] [,8559] [,8560] [,8561] [,8562] [,8563] [,8564] [,8565] [,8566]
[1,] 1 0 1 1 0 0 0 0 0
[2,] 0 0 0 1 1 0 1 1 0
[3,] 0 1 0 0 0 0 0 0 0
[4,] 0 0 1 0 0 0 0 0 1
[5,] 0 1 0 0 1 0 1 0 0
[6,] 0 0 1 0 0 0 0 0 0
[7,] 1 1 0 0 0 0 1 0 0
[8,] 0 1 0 0 0 0 0 1 1
[9,] 0 0 0 1 1 1 0 0 0
[10,] 0 1 0 0 0 0 0 0 0
[,8567] [,8568] [,8569] [,8570] [,8571] [,8572] [,8573] [,8574] [,8575]
[1,] 0 0 0 0 1 0 0 1 1
[2,] 0 0 0 0 0 0 0 0 0
[3,] 0 0 0 0 0 0 0 0 0
[4,] 0 0 0 1 0 0 0 0 0
[5,] 1 0 0 0 1 1 0 0 1
[6,] 1 0 0 0 0 1 0 0 0
[7,] 1 1 0 0 0 1 0 0 0
[8,] 0 0 0 0 1 1 1 0 1
[9,] 0 1 1 0 0 1 0 0 1
[10,] 0 0 0 1 1 0 0 0 0
[,8576] [,8577] [,8578] [,8579] [,8580] [,8581] [,8582] [,8583] [,8584]
[1,] 1 0 0 0 0 0 0 0 0
[2,] 0 0 0 0 1 0 0 0 0
[3,] 0 0 0 0 1 0 1 0 1
[4,] 0 0 0 0 0 1 0 0 1
[5,] 0 1 0 0 1 0 0 0 1
[6,] 0 0 0 0 0 1 0 0 1
[7,] 0 0 1 0 0 0 0 0 1
[8,] 1 0 0 0 0 0 0 0 0
[9,] 0 1 0 0 0 0 1 1 0
[10,] 0 0 0 0 1 1 0 0 1
[,8585] [,8586] [,8587] [,8588] [,8589] [,8590] [,8591] [,8592] [,8593]
[1,] 0 0 0 1 0 1 1 0 0
[2,] 1 0 0 1 0 1 0 0 0
[3,] 1 0 0 0 0 1 0 0 0
[4,] 0 0 0 1 1 1 1 0 0
[5,] 1 0 1 0 0 1 1 0 0
[6,] 1 0 1 0 0 1 0 0 0
[7,] 0 0 0 0 1 0 0 0 0
[8,] 0 0 1 0 0 0 0 0 0
[9,] 0 0 1 1 1 0 0 0 0
[10,] 0 0 0 0 0 1 0 0 0
[,8594] [,8595] [,8596] [,8597] [,8598] [,8599] [,8600] [,8601] [,8602]
[1,] 0 0 1 0 0 0 1 0 1
[2,] 0 0 1 0 0 1 1 1 1
[3,] 0 0 0 1 0 0 0 1 0
[4,] 0 0 0 1 0 0 1 1 0
[5,] 0 0 0 0 0 1 0 0 0
[6,] 0 0 1 1 1 0 1 0 0
[7,] 1 0 0 1 0 1 1 0 1
[8,] 1 0 0 0 1 0 0 0 0
[9,] 1 0 0 0 0 1 1 0 0
[10,] 0 1 0 0 0 0 0 1 1
[,8603] [,8604] [,8605] [,8606] [,8607] [,8608] [,8609] [,8610] [,8611]
[1,] 0 0 0 0 0 1 0 0 1
[2,] 0 0 0 0 0 0 0 0 1
[3,] 0 1 0 1 1 0 1 1 0
[4,] 0 0 0 0 0 0 0 0 0
[5,] 0 0 0 1 0 0 0 0 0
[6,] 1 0 0 0 0 0 0 1 1
[7,] 1 0 0 0 0 0 1 0 0
[8,] 0 1 1 0 1 1 0 0 0
[9,] 1 0 0 1 1 0 0 0 0
[10,] 0 0 0 1 1 0 0 0 0
[,8612] [,8613] [,8614] [,8615] [,8616] [,8617] [,8618] [,8619] [,8620]
[1,] 1 1 0 0 1 0 1 1 0
[2,] 0 0 0 1 0 0 0 0 0
[3,] 0 1 0 0 0 0 0 1 0
[4,] 0 1 0 0 0 1 0 0 0
[5,] 1 0 1 0 1 0 0 0 0
[6,] 0 0 0 0 0 0 1 0 0
[7,] 0 0 0 0 1 0 0 1 0
[8,] 0 0 1 0 0 0 0 0 0
[9,] 0 0 0 0 0 0 0 0 1
[10,] 1 1 0 1 0 0 1 0 0
[,8621] [,8622] [,8623] [,8624] [,8625] [,8626] [,8627] [,8628] [,8629]
[1,] 0 0 0 0 0 0 0 1 0
[2,] 0 0 0 1 0 0 1 0 0
[3,] 0 1 0 1 0 0 0 1 0
[4,] 1 0 1 0 0 1 0 0 0
[5,] 0 1 1 1 0 0 0 1 1
[6,] 0 1 1 1 1 0 0 0 0
[7,] 1 0 1 1 0 0 0 0 1
[8,] 0 0 1 0 0 1 1 0 0
[9,] 1 0 0 0 0 0 0 0 0
[10,] 1 1 0 0 0 0 0 0 0
[,8630] [,8631] [,8632] [,8633] [,8634] [,8635] [,8636] [,8637] [,8638]
[1,] 1 1 1 0 1 0 1 0 1
[2,] 0 0 0 0 1 0 0 0 0
[3,] 1 1 0 0 0 0 0 0 1
[4,] 0 0 0 0 1 0 0 1 1
[5,] 0 1 0 0 0 1 1 0 1
[6,] 0 0 0 1 1 1 0 0 0
[7,] 0 0 1 0 0 1 0 0 0
[8,] 0 0 0 0 0 0 0 1 1
[9,] 0 0 0 0 0 0 0 0 1
[10,] 0 1 1 0 0 1 0 1 1
[,8639] [,8640] [,8641] [,8642] [,8643] [,8644] [,8645] [,8646] [,8647]
[1,] 0 0 1 0 0 0 0 1 0
[2,] 0 0 0 0 1 0 0 0 0
[3,] 1 0 0 0 1 0 0 1 0
[4,] 1 1 0 0 0 0 0 0 1
[5,] 0 0 1 0 0 1 0 0 0
[6,] 1 0 0 0 1 0 0 0 0
[7,] 1 0 1 0 1 1 1 0 0
[8,] 0 0 0 1 0 0 0 1 1
[9,] 0 1 0 0 0 0 0 0 1
[10,] 0 0 0 0 1 0 1 0 1
[,8648] [,8649] [,8650] [,8651] [,8652] [,8653] [,8654] [,8655] [,8656]
[1,] 0 1 0 1 0 0 0 0 1
[2,] 0 1 1 1 0 0 1 0 0
[3,] 1 0 0 1 0 0 0 0 0
[4,] 0 0 1 0 0 0 0 1 1
[5,] 0 0 0 0 0 0 1 1 0
[6,] 1 1 0 1 0 0 0 1 0
[7,] 0 0 1 1 1 0 0 0 1
[8,] 0 0 1 0 0 1 1 1 1
[9,] 0 0 0 0 0 1 1 0 0
[10,] 0 0 1 1 0 1 0 0 0
[,8657] [,8658] [,8659] [,8660] [,8661] [,8662] [,8663] [,8664] [,8665]
[1,] 0 0 0 0 0 1 1 0 0
[2,] 1 0 0 0 0 1 0 1 1
[3,] 0 0 0 1 0 0 1 1 0
[4,] 0 0 0 0 1 0 0 1 0
[5,] 0 0 0 0 0 1 1 1 0
[6,] 0 0 0 0 1 0 0 1 1
[7,] 0 1 0 0 0 0 0 1 0
[8,] 0 0 0 0 0 1 0 0 0
[9,] 0 0 0 0 0 0 0 0 0
[10,] 0 1 1 0 0 1 0 1 0
[,8666] [,8667] [,8668] [,8669] [,8670] [,8671] [,8672] [,8673] [,8674]
[1,] 1 0 0 0 1 0 1 1 0
[2,] 0 0 0 0 0 1 0 1 1
[3,] 1 1 0 0 0 0 0 0 0
[4,] 0 1 1 0 0 0 1 0 0
[5,] 0 1 0 1 0 0 0 1 0
[6,] 0 0 0 1 0 0 0 1 0
[7,] 1 0 1 0 0 0 0 0 0
[8,] 0 0 0 0 0 0 0 1 0
[9,] 0 0 1 1 1 0 0 0 0
[10,] 0 0 1 0 0 0 0 0 0
[,8675] [,8676] [,8677] [,8678] [,8679] [,8680] [,8681] [,8682] [,8683]
[1,] 0 1 0 1 0 1 0 0 0
[2,] 0 0 1 0 0 0 0 0 1
[3,] 0 0 0 0 0 0 0 0 0
[4,] 0 1 1 0 0 0 0 0 0
[5,] 0 1 0 0 1 0 0 0 0
[6,] 0 0 1 0 1 1 0 0 1
[7,] 0 1 0 1 0 0 0 1 0
[8,] 1 1 1 0 1 0 0 0 0
[9,] 0 0 1 0 0 1 0 1 0
[10,] 0 0 0 0 0 0 0 1 0
[,8684] [,8685] [,8686] [,8687] [,8688] [,8689] [,8690] [,8691] [,8692]
[1,] 0 0 0 0 0 0 1 0 0
[2,] 0 0 1 0 0 0 0 1 0
[3,] 0 0 1 0 0 0 0 0 1
[4,] 0 0 0 0 0 0 0 0 0
[5,] 1 0 0 0 0 1 0 0 0
[6,] 1 0 0 0 0 0 0 0 0
[7,] 0 0 1 1 1 0 0 0 0
[8,] 0 0 0 1 1 0 0 0 1
[9,] 0 0 0 0 0 0 0 1 0
[10,] 0 0 0 0 0 0 0 1 0
[,8693] [,8694] [,8695] [,8696] [,8697] [,8698] [,8699] [,8700] [,8701]
[1,] 0 0 1 0 0 0 0 0 0
[2,] 0 0 0 0 1 1 0 0 0
[3,] 0 0 0 0 0 1 0 0 1
[4,] 0 0 0 0 1 0 0 0 0
[5,] 0 0 0 0 1 1 0 1 1
[6,] 0 0 0 0 1 0 1 0 0
[7,] 0 0 1 1 0 1 0 0 1
[8,] 0 0 0 0 0 0 0 0 0
[9,] 0 0 0 1 0 1 0 0 0
[10,] 0 0 1 0 0 1 0 1 0
[,8702] [,8703] [,8704] [,8705] [,8706] [,8707] [,8708] [,8709] [,8710]
[1,] 0 0 1 0 1 0 1 1 0
[2,] 0 1 0 1 1 0 0 0 1
[3,] 0 0 0 0 0 1 1 0 0
[4,] 0 0 0 1 0 0 0 0 0
[5,] 0 1 0 1 1 0 0 0 0
[6,] 0 0 1 0 0 0 0 0 0
[7,] 1 1 1 0 0 1 1 0 1
[8,] 0 0 1 0 0 0 0 0 0
[9,] 0 0 0 0 0 0 0 0 1
[10,] 1 0 1 0 0 0 1 1 0
[,8711] [,8712] [,8713] [,8714] [,8715] [,8716] [,8717] [,8718] [,8719]
[1,] 1 0 0 0 1 1 0 0 0
[2,] 1 0 0 1 1 0 0 1 0
[3,] 0 0 0 0 1 0 1 0 0
[4,] 0 0 0 1 0 0 0 1 0
[5,] 0 0 0 1 0 1 0 1 0
[6,] 0 1 0 1 1 1 1 0 0
[7,] 0 0 1 1 0 1 1 0 0
[8,] 1 0 1 0 1 0 0 0 0
[9,] 0 1 0 1 0 1 0 1 1
[10,] 0 0 0 1 0 0 0 0 1
[,8720] [,8721] [,8722] [,8723] [,8724] [,8725] [,8726] [,8727] [,8728]
[1,] 1 0 0 0 0 0 0 0 0
[2,] 1 1 0 1 0 0 0 0 0
[3,] 1 0 0 0 0 0 0 0 0
[4,] 0 1 1 0 0 0 0 0 0
[5,] 0 0 0 0 1 0 0 1 1
[6,] 0 0 0 0 1 1 0 0 0
[7,] 0 0 1 1 0 0 0 0 0
[8,] 0 0 0 0 1 0 0 1 0
[9,] 0 0 1 0 1 1 0 0 1
[10,] 0 0 0 0 1 0 1 0 0
[,8729] [,8730] [,8731] [,8732] [,8733] [,8734] [,8735] [,8736] [,8737]
[1,] 0 1 0 0 0 1 1 0 1
[2,] 0 1 1 0 0 0 0 0 0
[3,] 0 0 0 0 0 0 0 1 0
[4,] 0 1 0 0 0 0 0 0 1
[5,] 0 1 0 0 1 1 0 0 1
[6,] 0 0 0 1 0 0 0 0 0
[7,] 0 0 0 0 0 1 0 1 0
[8,] 1 0 0 0 1 0 0 0 0
[9,] 0 1 0 1 0 0 0 0 0
[10,] 0 0 0 0 1 0 1 1 0
[,8738] [,8739] [,8740] [,8741] [,8742] [,8743] [,8744] [,8745] [,8746]
[1,] 0 0 1 0 1 1 0 0 0
[2,] 1 0 1 0 1 0 0 0 0
[3,] 1 1 0 0 0 0 0 0 1
[4,] 0 1 0 0 1 0 1 0 0
[5,] 0 1 0 0 0 1 0 1 0
[6,] 0 0 1 0 0 1 1 1 0
[7,] 1 0 0 0 1 0 0 0 0
[8,] 0 0 1 0 1 0 0 1 0
[9,] 0 0 0 1 1 0 0 0 0
[10,] 1 1 1 0 0 0 0 0 0
[,8747] [,8748] [,8749] [,8750] [,8751] [,8752] [,8753] [,8754] [,8755]
[1,] 1 0 0 1 0 1 1 0 0
[2,] 0 0 0 0 0 0 1 0 0
[3,] 0 0 0 1 1 0 0 0 1
[4,] 0 0 1 0 0 0 0 0 0
[5,] 0 1 0 0 1 1 0 0 0
[6,] 1 1 0 0 0 1 0 0 0
[7,] 1 0 0 0 1 0 1 0 0
[8,] 0 0 0 1 0 0 0 0 0
[9,] 0 1 0 0 0 0 0 0 1
[10,] 1 0 0 0 0 0 1 1 0
[,8756] [,8757] [,8758] [,8759] [,8760] [,8761] [,8762] [,8763] [,8764]
[1,] 1 1 0 0 0 1 0 0 1
[2,] 0 0 0 0 0 0 0 0 0
[3,] 0 1 0 0 0 0 0 0 1
[4,] 0 0 1 1 0 0 0 0 0
[5,] 0 0 0 0 0 0 0 1 0
[6,] 0 0 1 0 0 0 1 0 0
[7,] 0 0 0 0 0 0 0 1 0
[8,] 0 0 0 0 0 0 0 0 0
[9,] 0 1 0 1 0 0 0 0 0
[10,] 0 0 0 0 0 0 1 0 0
[,8765] [,8766] [,8767] [,8768] [,8769] [,8770] [,8771] [,8772] [,8773]
[1,] 1 0 0 0 0 1 0 0 1
[2,] 0 0 0 1 1 1 0 0 0
[3,] 0 0 1 1 1 0 0 1 0
[4,] 1 0 0 1 0 0 1 0 0
[5,] 0 0 1 0 0 1 0 0 0
[6,] 0 0 0 0 0 0 0 0 0
[7,] 0 0 0 1 1 1 0 0 1
[8,] 0 0 1 0 0 0 0 1 0
[9,] 0 0 0 0 0 0 0 0 0
[10,] 0 0 0 0 0 0 1 0 0
[,8774] [,8775] [,8776] [,8777] [,8778] [,8779] [,8780] [,8781] [,8782]
[1,] 1 0 0 1 0 0 0 0 0
[2,] 0 0 0 0 0 0 0 0 0
[3,] 1 0 0 0 0 0 0 0 1
[4,] 1 0 0 0 0 1 1 0 1
[5,] 0 0 0 1 0 1 0 0 1
[6,] 0 0 0 0 0 1 0 0 0
[7,] 0 1 1 0 0 1 0 0 1
[8,] 0 0 0 0 0 0 0 0 1
[9,] 0 0 0 0 0 0 0 1 1
[10,] 1 0 0 0 0 0 0 0 0
[,8783] [,8784] [,8785] [,8786] [,8787] [,8788] [,8789] [,8790] [,8791]
[1,] 0 1 0 0 0 1 0 0 1
[2,] 1 1 1 0 0 0 0 0 0
[3,] 0 1 1 0 0 0 0 0 0
[4,] 0 0 0 0 1 0 0 1 0
[5,] 1 0 0 0 1 0 1 1 1
[6,] 0 0 0 0 0 0 1 0 0
[7,] 0 0 0 0 0 0 0 0 0
[8,] 0 0 0 1 0 0 0 0 0
[9,] 0 0 0 0 1 0 0 0 0
[10,] 0 0 1 1 0 1 0 0 0
[,8792] [,8793] [,8794] [,8795] [,8796] [,8797] [,8798] [,8799] [,8800]
[1,] 0 0 1 0 0 0 1 1 0
[2,] 1 0 0 0 0 0 0 0 0
[3,] 0 0 0 0 1 0 0 0 0
[4,] 1 0 1 0 1 1 0 0 0
[5,] 0 0 0 0 1 0 0 0 0
[6,] 1 0 1 0 0 1 0 0 1
[7,] 1 0 0 0 0 0 0 0 0
[8,] 0 0 1 0 0 0 0 0 0
[9,] 0 0 0 0 0 0 0 1 0
[10,] 0 0 0 0 0 0 1 0 0
[,8801] [,8802] [,8803] [,8804] [,8805] [,8806] [,8807] [,8808] [,8809]
[1,] 0 0 0 0 1 0 1 0 0
[2,] 0 0 0 0 0 0 0 1 1
[3,] 0 0 0 0 0 0 0 0 0
[4,] 0 1 0 0 0 0 0 1 0
[5,] 1 0 0 0 0 1 0 0 0
[6,] 0 0 0 1 0 0 0 1 0
[7,] 0 0 0 0 1 1 0 0 1
[8,] 1 0 0 0 1 0 0 0 0
[9,] 1 0 1 0 0 0 0 0 1
[10,] 1 0 0 0 0 0 0 0 1
[,8810] [,8811] [,8812] [,8813] [,8814] [,8815] [,8816] [,8817] [,8818]
[1,] 0 0 0 0 0 0 1 0 0
[2,] 0 0 1 0 0 0 0 0 0
[3,] 0 0 1 0 0 0 0 0 0
[4,] 0 1 0 0 0 0 0 0 0
[5,] 0 0 1 0 0 0 1 1 0
[6,] 1 0 0 0 1 1 0 0 0
[7,] 0 0 0 0 0 1 0 0 0
[8,] 0 0 0 0 0 0 0 0 1
[9,] 1 0 0 0 0 0 0 0 1
[10,] 0 0 1 1 1 0 0 1 0
[,8819] [,8820] [,8821] [,8822] [,8823] [,8824] [,8825] [,8826] [,8827]
[1,] 1 0 0 0 1 1 0 1 0
[2,] 1 0 0 0 1 0 1 1 0
[3,] 0 1 0 0 0 0 0 0 0
[4,] 0 0 0 0 0 0 0 0 0
[5,] 0 0 0 0 0 1 1 0 0
[6,] 0 1 0 0 0 0 0 0 0
[7,] 0 0 0 1 0 1 1 0 1
[8,] 0 0 0 0 1 0 1 0 0
[9,] 0 0 0 0 0 0 1 1 0
[10,] 0 0 0 0 0 0 0 1 0
[,8828] [,8829] [,8830] [,8831] [,8832] [,8833] [,8834] [,8835] [,8836]
[1,] 1 0 0 0 0 1 1 0 0
[2,] 0 0 1 0 1 0 0 0 1
[3,] 0 1 0 0 0 0 0 1 0
[4,] 0 1 0 0 0 0 1 1 1
[5,] 0 1 1 0 0 0 1 0 0
[6,] 0 0 0 0 0 0 0 0 0
[7,] 0 1 1 1 1 1 0 1 0
[8,] 0 1 0 0 1 0 0 0 0
[9,] 0 0 0 0 0 0 0 0 0
[10,] 0 1 0 1 0 1 1 0 1
[,8837] [,8838] [,8839] [,8840] [,8841] [,8842] [,8843] [,8844] [,8845]
[1,] 0 1 1 0 0 0 0 0 0
[2,] 0 0 1 0 0 0 0 0 1
[3,] 1 1 0 0 0 0 0 0 1
[4,] 1 1 0 0 0 0 0 0 0
[5,] 0 0 0 0 0 0 0 0 0
[6,] 0 0 1 0 0 1 1 1 0
[7,] 0 0 1 0 0 0 0 0 0
[8,] 1 0 0 0 0 0 0 0 0
[9,] 1 0 0 0 0 1 0 0 0
[10,] 0 0 0 0 0 0 0 1 0
[,8846] [,8847] [,8848] [,8849] [,8850] [,8851] [,8852] [,8853] [,8854]
[1,] 0 0 0 1 0 1 0 0 1
[2,] 0 0 0 1 0 0 0 0 0
[3,] 0 1 0 0 0 0 1 0 0
[4,] 1 1 0 0 0 0 0 0 0
[5,] 0 0 0 0 1 0 0 0 0
[6,] 0 0 0 0 0 0 1 1 1
[7,] 0 1 0 0 0 0 0 0 1
[8,] 0 0 0 0 1 1 0 0 0
[9,] 0 0 0 0 0 0 1 0 0
[10,] 0 0 1 0 0 0 0 0 0
[,8855] [,8856] [,8857] [,8858] [,8859] [,8860] [,8861] [,8862] [,8863]
[1,] 0 1 1 0 0 0 1 0 0
[2,] 0 0 1 0 1 0 1 1 1
[3,] 0 1 0 0 0 0 0 0 0
[4,] 0 0 0 0 1 0 1 0 0
[5,] 0 0 0 0 0 1 1 1 0
[6,] 0 0 0 0 0 1 0 1 0
[7,] 1 0 1 0 1 1 0 0 0
[8,] 0 0 1 1 0 0 1 0 0
[9,] 0 1 0 0 0 0 1 1 1
[10,] 1 1 0 1 0 0 1 0 0
[,8864] [,8865] [,8866] [,8867] [,8868] [,8869] [,8870] [,8871] [,8872]
[1,] 0 0 1 0 0 1 1 0 0
[2,] 0 0 0 0 1 0 0 1 0
[3,] 1 0 0 1 1 1 0 0 0
[4,] 0 0 0 1 1 1 0 0 0
[5,] 1 1 0 0 0 0 0 0 0
[6,] 0 0 0 0 1 0 1 0 0
[7,] 0 0 0 0 1 0 0 1 0
[8,] 1 0 0 0 0 0 0 0 0
[9,] 1 0 0 0 0 0 1 1 0
[10,] 0 0 0 0 0 1 0 0 0
[,8873] [,8874] [,8875] [,8876] [,8877] [,8878] [,8879] [,8880] [,8881]
[1,] 0 1 1 1 0 1 0 0 1
[2,] 0 0 0 0 0 0 0 0 0
[3,] 0 0 0 0 0 0 1 0 0
[4,] 0 0 1 1 0 1 0 1 0
[5,] 0 1 0 0 1 0 0 0 1
[6,] 0 0 0 1 0 1 1 1 1
[7,] 0 0 0 0 0 1 0 1 1
[8,] 0 0 0 0 1 0 0 1 0
[9,] 0 1 1 0 1 0 1 0 0
[10,] 0 0 0 1 0 0 1 0 1
[,8882] [,8883] [,8884] [,8885] [,8886] [,8887] [,8888] [,8889] [,8890]
[1,] 1 0 0 1 1 0 1 0 1
[2,] 0 0 0 0 1 1 0 0 0
[3,] 0 1 0 0 0 0 0 0 0
[4,] 0 0 0 0 0 0 0 1 0
[5,] 0 1 1 0 1 0 0 1 0
[6,] 0 0 0 0 0 1 0 1 1
[7,] 0 0 0 0 0 1 0 0 0
[8,] 0 0 0 0 0 0 0 0 0
[9,] 0 0 0 0 0 1 0 0 0
[10,] 1 0 0 0 0 0 0 1 1
[,8891] [,8892] [,8893] [,8894] [,8895] [,8896] [,8897] [,8898] [,8899]
[1,] 0 0 0 0 1 0 0 0 0
[2,] 0 0 0 0 0 1 1 0 1
[3,] 0 1 1 0 0 0 0 1 0
[4,] 0 1 0 1 1 0 0 0 0
[5,] 1 0 0 0 0 0 0 0 0
[6,] 1 0 0 0 0 0 1 0 0
[7,] 1 0 0 1 1 0 0 1 0
[8,] 0 0 0 0 1 1 0 0 0
[9,] 0 0 0 1 0 1 1 0 0
[10,] 0 0 0 0 0 0 1 1 0
[,8900] [,8901] [,8902] [,8903] [,8904] [,8905] [,8906] [,8907] [,8908]
[1,] 0 0 1 0 1 0 0 0 1
[2,] 0 0 0 1 1 0 1 0 1
[3,] 0 0 0 0 1 0 1 0 0
[4,] 0 0 0 0 0 0 0 1 0
[5,] 0 1 0 0 0 0 0 0 1
[6,] 1 0 0 0 0 1 0 1 0
[7,] 1 0 1 1 1 0 0 1 0
[8,] 0 1 1 0 0 1 0 0 0
[9,] 0 1 0 0 0 0 0 0 0
[10,] 0 0 0 1 1 0 0 0 1
[,8909] [,8910] [,8911] [,8912] [,8913] [,8914] [,8915] [,8916] [,8917]
[1,] 0 0 0 1 1 0 0 0 1
[2,] 0 0 0 0 1 1 0 1 0
[3,] 1 0 0 1 0 0 0 0 1
[4,] 0 0 1 0 0 0 0 0 0
[5,] 1 0 1 0 1 0 1 0 0
[6,] 0 0 0 0 0 0 1 1 0
[7,] 0 0 0 0 0 0 0 1 1
[8,] 1 0 0 0 1 1 0 0 0
[9,] 0 1 0 0 0 0 1 0 0
[10,] 0 0 0 0 0 0 0 1 0
[,8918] [,8919] [,8920] [,8921] [,8922] [,8923] [,8924] [,8925] [,8926]
[1,] 0 0 0 0 0 1 1 0 0
[2,] 0 0 1 0 0 0 1 1 0
[3,] 1 1 1 0 0 0 1 0 1
[4,] 0 0 0 0 0 0 0 0 0
[5,] 0 0 0 0 0 0 0 0 0
[6,] 0 1 0 1 0 0 0 0 0
[7,] 1 0 1 0 0 1 0 0 0
[8,] 0 0 0 0 0 0 1 0 0
[9,] 0 0 1 1 0 1 0 0 0
[10,] 0 0 0 1 0 0 0 0 1
[,8927] [,8928] [,8929] [,8930] [,8931] [,8932] [,8933] [,8934] [,8935]
[1,] 0 0 0 0 1 0 0 0 0
[2,] 0 0 1 1 1 0 1 0 0
[3,] 0 1 1 0 1 1 1 0 1
[4,] 0 0 0 1 0 0 1 0 0
[5,] 1 1 0 0 0 0 0 1 0
[6,] 0 0 0 0 1 0 1 0 0
[7,] 1 0 1 0 0 0 0 0 0
[8,] 1 0 0 1 1 0 0 1 0
[9,] 0 1 0 1 0 0 0 0 0
[10,] 0 0 0 0 0 0 0 0 0
[,8936] [,8937] [,8938] [,8939] [,8940] [,8941] [,8942] [,8943] [,8944]
[1,] 0 1 0 1 0 0 0 0 1
[2,] 0 0 1 1 0 1 1 1 0
[3,] 0 0 0 1 0 0 0 0 1
[4,] 0 0 0 1 0 0 1 0 1
[5,] 0 0 0 0 0 0 1 0 0
[6,] 1 0 1 0 0 0 1 0 0
[7,] 1 1 0 1 0 0 0 0 0
[8,] 0 0 1 0 1 0 0 0 0
[9,] 0 0 0 0 1 0 1 0 0
[10,] 0 0 0 0 0 0 0 1 1
[,8945] [,8946] [,8947] [,8948] [,8949] [,8950] [,8951] [,8952] [,8953]
[1,] 0 1 0 0 0 0 0 0 0
[2,] 0 0 1 0 0 0 1 0 0
[3,] 1 1 0 0 0 0 1 0 0
[4,] 0 1 0 1 0 0 1 0 0
[5,] 0 0 0 0 0 1 1 0 0
[6,] 0 1 0 0 0 1 0 0 1
[7,] 0 0 0 0 0 0 1 0 0
[8,] 1 0 0 1 1 0 0 1 0
[9,] 1 0 1 1 0 0 0 0 1
[10,] 0 1 0 0 0 0 0 1 0
[,8954] [,8955] [,8956] [,8957] [,8958] [,8959] [,8960] [,8961] [,8962]
[1,] 0 0 1 0 0 1 0 0 0
[2,] 0 0 0 0 0 0 0 0 0
[3,] 1 0 0 0 0 0 0 0 0
[4,] 1 0 1 0 0 0 1 0 0
[5,] 1 0 0 1 0 0 0 0 0
[6,] 0 0 1 0 0 0 1 0 0
[7,] 0 0 1 0 1 0 0 0 0
[8,] 0 0 1 0 0 1 1 0 0
[9,] 0 0 0 0 0 0 0 0 0
[10,] 0 0 0 0 0 0 0 0 0
[,8963] [,8964] [,8965] [,8966] [,8967] [,8968] [,8969] [,8970] [,8971]
[1,] 0 0 0 1 0 0 0 1 0
[2,] 1 0 0 0 1 0 0 0 0
[3,] 0 0 0 0 0 1 0 0 0
[4,] 0 1 0 0 0 0 1 0 0
[5,] 0 0 1 0 0 0 0 0 0
[6,] 1 1 0 1 0 0 0 1 0
[7,] 0 1 0 0 0 1 0 0 0
[8,] 0 0 0 0 1 0 1 0 0
[9,] 1 1 0 0 0 0 0 0 0
[10,] 0 1 0 1 0 0 0 0 0
[,8972] [,8973] [,8974] [,8975] [,8976] [,8977] [,8978] [,8979] [,8980]
[1,] 1 1 0 1 0 1 0 0 1
[2,] 0 0 0 0 0 0 0 0 1
[3,] 0 0 0 1 0 0 1 0 0
[4,] 0 0 1 1 0 0 0 0 1
[5,] 0 0 1 1 1 0 0 0 1
[6,] 0 1 1 1 1 0 0 0 1
[7,] 0 0 1 0 0 0 0 0 0
[8,] 0 1 0 0 0 0 0 0 0
[9,] 0 0 0 0 0 0 0 1 0
[10,] 0 1 0 0 0 0 0 0 0
[,8981] [,8982] [,8983] [,8984] [,8985] [,8986] [,8987] [,8988] [,8989]
[1,] 0 0 0 1 0 1 0 0 0
[2,] 1 0 0 0 1 0 0 0 1
[3,] 0 0 0 1 0 0 0 0 0
[4,] 1 1 0 1 1 0 0 0 0
[5,] 0 0 0 0 0 0 0 0 1
[6,] 0 0 0 0 0 0 0 0 1
[7,] 0 0 0 1 1 0 1 0 1
[8,] 1 1 0 0 1 1 0 0 0
[9,] 0 0 0 0 0 0 0 0 1
[10,] 0 0 0 0 1 0 1 0 0
[,8990] [,8991] [,8992] [,8993] [,8994] [,8995] [,8996] [,8997] [,8998]
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[,8999] [,9000] [,9001] [,9002] [,9003] [,9004] [,9005] [,9006] [,9007]
[1,] 0 0 1 0 0 1 0 1 0
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[8,] 0 1 0 0 0 1 0 0 1
[9,] 0 1 1 0 1 0 1 0 0
[10,] 0 0 1 1 0 0 1 0 0
[,9008] [,9009] [,9010] [,9011] [,9012] [,9013] [,9014] [,9015] [,9016]
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[8,] 0 0 0 0 1 1 1 0 0
[9,] 0 0 0 0 0 0 0 0 0
[10,] 0 0 0 0 0 0 0 1 1
[,9017] [,9018] [,9019] [,9020] [,9021] [,9022] [,9023] [,9024] [,9025]
[1,] 1 0 0 0 0 0 0 0 1
[2,] 0 0 1 1 1 0 0 0 0
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[8,] 0 0 0 0 1 0 0 0 1
[9,] 0 1 1 1 0 0 1 0 0
[10,] 1 0 1 1 0 1 0 0 1
[,9026] [,9027] [,9028] [,9029] [,9030] [,9031] [,9032] [,9033] [,9034]
[1,] 0 0 0 1 0 0 0 1 1
[2,] 1 0 0 0 1 0 0 0 1
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[8,] 0 0 0 0 0 1 0 0 0
[9,] 0 0 1 0 0 0 0 0 0
[10,] 0 0 0 0 0 0 1 1 0
[,9035] [,9036] [,9037] [,9038] [,9039] [,9040] [,9041] [,9042] [,9043]
[1,] 0 1 0 0 0 1 0 0 0
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[8,] 1 1 0 0 0 1 0 0 1
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[10,] 0 0 0 0 1 0 0 0 0
[,9044] [,9045] [,9046] [,9047] [,9048] [,9049] [,9050] [,9051] [,9052]
[1,] 1 1 0 0 0 0 0 0 0
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[7,] 1 1 0 1 1 0 0 1 0
[8,] 0 1 0 0 0 0 1 0 1
[9,] 1 0 1 0 0 1 0 0 1
[10,] 0 0 1 0 0 0 0 1 0
[,9053] [,9054] [,9055] [,9056] [,9057] [,9058] [,9059] [,9060] [,9061]
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[9,] 1 1 0 0 0 1 0 0 1
[10,] 0 0 0 1 0 0 0 1 0
[,9062] [,9063] [,9064] [,9065] [,9066] [,9067] [,9068] [,9069] [,9070]
[1,] 0 0 1 0 0 0 1 0 0
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[8,] 0 1 1 0 0 0 1 0 0
[9,] 0 0 1 0 0 0 0 0 1
[10,] 1 0 0 0 0 0 1 1 1
[,9071] [,9072] [,9073] [,9074] [,9075] [,9076] [,9077] [,9078] [,9079]
[1,] 1 0 1 0 1 0 0 0 0
[2,] 1 0 0 0 0 0 0 1 1
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[10,] 0 1 0 1 0 0 0 1 0
[,9080] [,9081] [,9082] [,9083] [,9084] [,9085] [,9086] [,9087] [,9088]
[1,] 0 1 1 0 1 0 0 1 1
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[,9089] [,9090] [,9091] [,9092] [,9093] [,9094] [,9095] [,9096] [,9097]
[1,] 0 0 0 0 1 0 1 0 1
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[,9098] [,9099] [,9100] [,9101] [,9102] [,9103] [,9104] [,9105] [,9106]
[1,] 0 1 0 0 0 1 0 0 0
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[,9107] [,9108] [,9109] [,9110] [,9111] [,9112] [,9113] [,9114] [,9115]
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[,9116] [,9117] [,9118] [,9119] [,9120] [,9121] [,9122] [,9123] [,9124]
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[,9125] [,9126] [,9127] [,9128] [,9129] [,9130] [,9131] [,9132] [,9133]
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[,9134] [,9135] [,9136] [,9137] [,9138] [,9139] [,9140] [,9141] [,9142]
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[10,] 1 0 0 1 1 0 0 0 1
[,9143] [,9144] [,9145] [,9146] [,9147] [,9148] [,9149] [,9150] [,9151]
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[,9152] [,9153] [,9154] [,9155] [,9156] [,9157] [,9158] [,9159] [,9160]
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[,9161] [,9162] [,9163] [,9164] [,9165] [,9166] [,9167] [,9168] [,9169]
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[,9170] [,9171] [,9172] [,9173] [,9174] [,9175] [,9176] [,9177] [,9178]
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[,9179] [,9180] [,9181] [,9182] [,9183] [,9184] [,9185] [,9186] [,9187]
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[,9188] [,9189] [,9190] [,9191] [,9192] [,9193] [,9194] [,9195] [,9196]
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[,9197] [,9198] [,9199] [,9200] [,9201] [,9202] [,9203] [,9204] [,9205]
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[,9206] [,9207] [,9208] [,9209] [,9210] [,9211] [,9212] [,9213] [,9214]
[1,] 1 0 1 0 1 1 0 0 0
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[,9215] [,9216] [,9217] [,9218] [,9219] [,9220] [,9221] [,9222] [,9223]
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[,9224] [,9225] [,9226] [,9227] [,9228] [,9229] [,9230] [,9231] [,9232]
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[,9233] [,9234] [,9235] [,9236] [,9237] [,9238] [,9239] [,9240] [,9241]
[1,] 1 0 1 0 0 0 0 0 0
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[10,] 1 1 1 0 0 1 0 0 0
[,9242] [,9243] [,9244] [,9245] [,9246] [,9247] [,9248] [,9249] [,9250]
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[10,] 1 0 0 0 1 1 1 0 0
[,9251] [,9252] [,9253] [,9254] [,9255] [,9256] [,9257] [,9258] [,9259]
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[,9260] [,9261] [,9262] [,9263] [,9264] [,9265] [,9266] [,9267] [,9268]
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[10,] 1 0 0 1 0 0 1 0 1
[,9269] [,9270] [,9271] [,9272] [,9273] [,9274] [,9275] [,9276] [,9277]
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[,9278] [,9279] [,9280] [,9281] [,9282] [,9283] [,9284] [,9285] [,9286]
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[,9287] [,9288] [,9289] [,9290] [,9291] [,9292] [,9293] [,9294] [,9295]
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[,9296] [,9297] [,9298] [,9299] [,9300] [,9301] [,9302] [,9303] [,9304]
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[,9305] [,9306] [,9307] [,9308] [,9309] [,9310] [,9311] [,9312] [,9313]
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[,9314] [,9315] [,9316] [,9317] [,9318] [,9319] [,9320] [,9321] [,9322]
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[,9323] [,9324] [,9325] [,9326] [,9327] [,9328] [,9329] [,9330] [,9331]
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[,9332] [,9333] [,9334] [,9335] [,9336] [,9337] [,9338] [,9339] [,9340]
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[,9341] [,9342] [,9343] [,9344] [,9345] [,9346] [,9347] [,9348] [,9349]
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[,9350] [,9351] [,9352] [,9353] [,9354] [,9355] [,9356] [,9357] [,9358]
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[,9359] [,9360] [,9361] [,9362] [,9363] [,9364] [,9365] [,9366] [,9367]
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[,9368] [,9369] [,9370] [,9371] [,9372] [,9373] [,9374] [,9375] [,9376]
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[,9377] [,9378] [,9379] [,9380] [,9381] [,9382] [,9383] [,9384] [,9385]
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[,9386] [,9387] [,9388] [,9389] [,9390] [,9391] [,9392] [,9393] [,9394]
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[,9395] [,9396] [,9397] [,9398] [,9399] [,9400] [,9401] [,9402] [,9403]
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[,9404] [,9405] [,9406] [,9407] [,9408] [,9409] [,9410] [,9411] [,9412]
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[,9413] [,9414] [,9415] [,9416] [,9417] [,9418] [,9419] [,9420] [,9421]
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[,9422] [,9423] [,9424] [,9425] [,9426] [,9427] [,9428] [,9429] [,9430]
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[,9431] [,9432] [,9433] [,9434] [,9435] [,9436] [,9437] [,9438] [,9439]
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[,9440] [,9441] [,9442] [,9443] [,9444] [,9445] [,9446] [,9447] [,9448]
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[,9449] [,9450] [,9451] [,9452] [,9453] [,9454] [,9455] [,9456] [,9457]
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[,9458] [,9459] [,9460] [,9461] [,9462] [,9463] [,9464] [,9465] [,9466]
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[,9467] [,9468] [,9469] [,9470] [,9471] [,9472] [,9473] [,9474] [,9475]
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[,9476] [,9477] [,9478] [,9479] [,9480] [,9481] [,9482] [,9483] [,9484]
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[,9485] [,9486] [,9487] [,9488] [,9489] [,9490] [,9491] [,9492] [,9493]
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[,9494] [,9495] [,9496] [,9497] [,9498] [,9499] [,9500] [,9501] [,9502]
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[,9503] [,9504] [,9505] [,9506] [,9507] [,9508] [,9509] [,9510] [,9511]
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[,9512] [,9513] [,9514] [,9515] [,9516] [,9517] [,9518] [,9519] [,9520]
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[,9521] [,9522] [,9523] [,9524] [,9525] [,9526] [,9527] [,9528] [,9529]
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[,9530] [,9531] [,9532] [,9533] [,9534] [,9535] [,9536] [,9537] [,9538]
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[,9539] [,9540] [,9541] [,9542] [,9543] [,9544] [,9545] [,9546] [,9547]
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[,9548] [,9549] [,9550] [,9551] [,9552] [,9553] [,9554] [,9555] [,9556]
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[,9557] [,9558] [,9559] [,9560] [,9561] [,9562] [,9563] [,9564] [,9565]
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[,9566] [,9567] [,9568] [,9569] [,9570] [,9571] [,9572] [,9573] [,9574]
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[,9575] [,9576] [,9577] [,9578] [,9579] [,9580] [,9581] [,9582] [,9583]
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[,9584] [,9585] [,9586] [,9587] [,9588] [,9589] [,9590] [,9591] [,9592]
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[,9593] [,9594] [,9595] [,9596] [,9597] [,9598] [,9599] [,9600] [,9601]
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[,9602] [,9603] [,9604] [,9605] [,9606] [,9607] [,9608] [,9609] [,9610]
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[,9611] [,9612] [,9613] [,9614] [,9615] [,9616] [,9617] [,9618] [,9619]
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[,9620] [,9621] [,9622] [,9623] [,9624] [,9625] [,9626] [,9627] [,9628]
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[10,] 1 0 0 0 0 0 0 1 0
[,9629] [,9630] [,9631] [,9632] [,9633] [,9634] [,9635] [,9636] [,9637]
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[10,] 1 1 1 0 0 0 0 1 0
[,9638] [,9639] [,9640] [,9641] [,9642] [,9643] [,9644] [,9645] [,9646]
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[,9647] [,9648] [,9649] [,9650] [,9651] [,9652] [,9653] [,9654] [,9655]
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[,9656] [,9657] [,9658] [,9659] [,9660] [,9661] [,9662] [,9663] [,9664]
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[,9665] [,9666] [,9667] [,9668] [,9669] [,9670] [,9671] [,9672] [,9673]
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[,9674] [,9675] [,9676] [,9677] [,9678] [,9679] [,9680] [,9681] [,9682]
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[,9683] [,9684] [,9685] [,9686] [,9687] [,9688] [,9689] [,9690] [,9691]
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[,9692] [,9693] [,9694] [,9695] [,9696] [,9697] [,9698] [,9699] [,9700]
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[,9701] [,9702] [,9703] [,9704] [,9705] [,9706] [,9707] [,9708] [,9709]
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[,9710] [,9711] [,9712] [,9713] [,9714] [,9715] [,9716] [,9717] [,9718]
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[,9719] [,9720] [,9721] [,9722] [,9723] [,9724] [,9725] [,9726] [,9727]
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[,9728] [,9729] [,9730] [,9731] [,9732] [,9733] [,9734] [,9735] [,9736]
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[,9737] [,9738] [,9739] [,9740] [,9741] [,9742] [,9743] [,9744] [,9745]
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[10,] 0 0 1 0 1 0 0 1 0
[,9746] [,9747] [,9748] [,9749] [,9750] [,9751] [,9752] [,9753] [,9754]
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[,9755] [,9756] [,9757] [,9758] [,9759] [,9760] [,9761] [,9762] [,9763]
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[,9764] [,9765] [,9766] [,9767] [,9768] [,9769] [,9770] [,9771] [,9772]
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[,9773] [,9774] [,9775] [,9776] [,9777] [,9778] [,9779] [,9780] [,9781]
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[,9782] [,9783] [,9784] [,9785] [,9786] [,9787] [,9788] [,9789] [,9790]
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[,9791] [,9792] [,9793] [,9794] [,9795] [,9796] [,9797] [,9798] [,9799]
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[,9800] [,9801] [,9802] [,9803] [,9804] [,9805] [,9806] [,9807] [,9808]
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[,9809] [,9810] [,9811] [,9812] [,9813]
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[ reached getOption("max.print") -- omitted 3990 rows ]# Generate fitted draws directly with brms
fitted_draws <- fitted(
Diabetes_prior,
newdata = Imputed_data1,
summary = FALSE, # gives all posterior draws instead of summary
nsamples = 100 # limit to 100 draws
)
# Convert to long format manually
fitted_long <- data.frame(
BMI = rep(Imputed_data1$BMI, times = 100),
draw = rep(1:100, each = nrow(Imputed_data1)),
.value = as.vector(t(fitted_draws))
)
# BMI Plot the fitted lines
ggplot(fitted_long, aes(x = BMI, y = .value, group = draw)) +
geom_line(size = 0.1, alpha = 0.4) +
labs(y = "Predicted probability of Diabetes")
# Age
fitted_long <- data.frame(
Age = rep(Imputed_data1$Age, times = 100),
draw = rep(1:100, each = nrow(Imputed_data1)),
.value = as.vector(t(fitted_draws))
)
ggplot(fitted_long, aes(x = Age, y = .value, group = draw)) +
geom_line(size = 0.1, alpha = 0.4) +
labs(y = "Predicted probability of Diabetes tomorrow")
fitted_long <- data.frame(
Race = rep(Imputed_data1$Race, times = 100),
draw = rep(1:100, each = nrow(Imputed_data1)),
.value = as.vector(t(fitted_draws))
)
ggplot(fitted_long, aes(x = Race, y = .value, group = draw)) +
geom_line(size = 0.1, alpha = 0.4) +
labs(y = "Predicted probability of Diabetes tomorrow")
# Gender
fitted_long <- data.frame(
Gender = rep(Imputed_data1$Gender, times = 100),
draw = rep(1:100, each = nrow(Imputed_data1)),
.value = as.vector(t(fitted_draws))
)
ggplot(fitted_long, aes(x = Gender, y = .value, group = draw)) +
geom_line(size = 0.1, alpha = 0.4) +
labs(y = "Predicted probability of Diabetes tomorrow")
fitted_draws <- fitted(
Diabetes_prior,
newdata = Imputed_data1,
summary = FALSE, # gives all posterior draws instead of summary
nsamples = 100 # limit to 100 draws
)
# Convert to long format manually
fitted_long <- data.frame(
Race = rep(Imputed_data1$Race, times = 100),
draw = rep(1:100, each = nrow(Imputed_data1)),
.value = as.vector(t(fitted_draws)))
ggplot(fitted_long, aes(x = Race, y = .value, fill = Race)) +
geom_boxplot(alpha = 0.6) +
geom_jitter(width = 0.2, alpha = 0.3, size = 1) +
labs(x = "Race Category", y = "Predicted Probability of Diabetes",
title = "Predicted Diabetes Probability by Race Category") +
theme_minimal()
Comparing Models
Linear regression model on raw data
Multivariate logistic regression on imputed dataset (MI + MLR)
Bayesian Logistic Regression on imputed data
Summarize your key findings.
Discuss the implications of your results.